["itemContainer",{"xmlns:xsi":"http://www.w3.org/2001/XMLSchema-instance","xsi:schemaLocation":"http://omeka.org/schemas/omeka-xml/v5 http://omeka.org/schemas/omeka-xml/v5/omeka-xml-5-0.xsd","uri":"https://www.johnntowse.com/LUSTRE/items/browse?collection=3&output=omeka-json","accessDate":"2026-05-02T12:16:44+00:00"},["miscellaneousContainer",["pagination",["pageNumber","1"],["perPage","10"],["totalResults","3"]]],["item",{"itemId":"192","public":"1","featured":"0"},["fileContainer",["file",{"fileId":"212"},["src","https://www.johnntowse.com/LUSTRE/files/original/7d6c9cf5fdd98d716c94e889c243c0c0.pdf"],["authentication","fa4b33e4b92ee93a65616bbab7185e5c"]],["file",{"fileId":"213"},["src","https://www.johnntowse.com/LUSTRE/files/original/f11ffa6a464eee8a38144f043e6d8a06.pdf"],["authentication","9e37ad79ac89170b5ec0237b8d9230f6"]],["file",{"fileId":"214"},["src","https://www.johnntowse.com/LUSTRE/files/original/8093b4f91fa9d0452695e80ef3ecf6eb.pdf"],["authentication","671adccd1d64ac672834905ab18a0ce2"]]],["collection",{"collectionId":"3"},["elementSetContainer",["elementSet",{"elementSetId":"1"},["name","Dublin Core"],["description","The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/."],["elementContainer",["element",{"elementId":"50"},["name","Title"],["description","A name given to the resource"],["elementTextContainer",["elementText",{"elementTextId":"181"},["text","EEG"]]]],["element",{"elementId":"41"},["name","Description"],["description","An account of the resource"],["elementTextContainer",["elementText",{"elementTextId":"182"},["text","Electroencephalography (EEG) is a method for monitoring electrical activity in the brain. It uses electrodes placed on or below the scalp to record activity with coarse spatial but high temporal resolution"]]]]]]]],["itemType",{"itemTypeId":"14"},["name","Dataset"],["description","Data encoded in a defined structure. Examples include lists, tables, and databases. A dataset may be useful for direct machine processing."]],["elementSetContainer",["elementSet",{"elementSetId":"1"},["name","Dublin Core"],["description","The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/."],["elementContainer",["element",{"elementId":"50"},["name","Title"],["description","A name given to the resource"],["elementTextContainer",["elementText",{"elementTextId":"3832"},["text","N1 Adaptation: Exploring the Neuronal Basis of the Interaction Between Auditory Sensory Memory and Attention"]]]],["element",{"elementId":"39"},["name","Creator"],["description","An entity primarily responsible for making the resource"],["elementTextContainer",["elementText",{"elementTextId":"3833"},["text","Gengjie Jack Ho"]]]],["element",{"elementId":"40"},["name","Date"],["description","A point or period of time associated with an event in the lifecycle of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3834"},["text","2023"]]]],["element",{"elementId":"41"},["name","Description"],["description","An account of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3835"},["text","The aim was to explore whether voluntarily focusing on repetitive auditory stimuli influences the lifetime of N1 adaptation, which indexes the lifetime of auditory sensory memory. Twenty-six neurotypical participants with self-reported normal hearing were recruited from Lancaster University. Electroencephalogram (EEG) recording took place in a sound-attenuated laboratory. A two-by-two factorial design was employed, where one factor manipulated the presence or absence of attention, whereas the other factor manipulated the stimulus-onset interval (SOI), which primarily served to calculate the lifetime of adaptation. Three different amplitude measurement methods were used to calculate the N1 amplitude, therefore three sets of statistical analyses were performed for each investigation. For the preliminary investigation, two-way ANOVAs were conducted to evaluate the impact of attentional focus (presence or absence) and SOI (short or long) on the amplitude of N1. For the primary investigation, paired-samples t-tests were conducted to evaluate whether the presence or absence of attention influences the N1 adaptation lifetime. The preliminary results indicated no significant difference in N1 amplitude between the presence and absence of attentional focus. There was also no significant difference in the SOI, except for one of the amplitude measurement methods, which showed greater N1 amplitudes in the Long SOI condition. The primary results indicated that whether attention was present or not showed no significant effect on the adaptation lifetime across all three amplitude measurement methods. However, the study suffered from low statistical power and possible issues with the methodological design due to the combined use of visual and auditory modalities to manipulate attentional focus. Therefore, it is inappropriate to draw conclusions from the findings of this study. Methodological improvements and theoretical implications were discussed."]]]],["element",{"elementId":"49"},["name","Subject"],["description","The topic of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3836"},["text","neuropsychology, attention, auditory sensory memory, N1 adaptation, sensory processing, neural responses"]]]],["element",{"elementId":"48"},["name","Source"],["description","A related resource from which the described resource is derived"],["elementTextContainer",["elementText",{"elementTextId":"3837"},["text","Methods Section:\r\nParticipants\r\nTwenty-six neurotypical participants with self-reported normal hearing (9 males, 16 females, 1 prefer not to say), all of whom were students from Lancaster University, were recruited using opportunity sampling via advertising on social media platforms and SONA. The age range of the participants spanned from 18 to 34 years (M = 22.85, SD = 2.55). Sixteen participants were excluded due to excessive electric noise, resulting in a remaining pool of 10 participants. All participants provided written consent and volunteered to participate in the experiment. The study received ethical approval from Lancaster University’s Department of Psychology.\r\nStimuli\r\nThe experiment employed the oddball paradigm to elicit auditory responses. The standards were presented at a constant rate of 210 repetitions per condition, while the deviants appeared unpredictably at a 5% probability (10 deviants per condition). The sequence of standards and deviants remained consistent across all conditions. The standards were presented as a 500-Hz pure tone, while the deviants were a 503-Hz pure tone. The duration of each tone was 100 milliseconds, with 10 milliseconds of linear onset and offset ramps. All tones were presented at a consistent and comfortable volume level (28% volume on Windows 10). The auditory stimuli were programmed and delivered using MATLAB.\r\nDesign\r\nThe study followed a two-by-two factorial design (see Figure 1). It included two attention conditions: Active and Passive. In the Active condition, participants were presented with a stream of standards and deviants while focusing on a fixation cross. Their objective was to count the occurrences of deviants. In the Passive condition, participants viewed a nature documentary displayed on a smartphone screen. Their objective was to count the number of animal species featured in the documentary while ignoring the stream of auditory stimuli playing simultaneously in the background. Both the fixation cross and the smartphone screen were positioned one metre in front of the participants. Additionally, there were two SOI conditions: Short SOI (1.7 seconds) and Long SOI (3.4 seconds). The oddball paradigm was integrated into a stimulus block design - with two types of stimulus blocks, each having a specific SOI. Note that the order of the conditions was randomized among participants.\r\nThe purpose of the design was to manipulate attention towards repetitive auditory stimuli and calculate adaptation lifetime. The counting tasks in the Active and Passive conditions manipulated attentional focus. In the Active condition, the count-the-deviants task aimed to maintain participants’ attention on the repetitive auditory stimuli. In the Passive condition, the count-the-animal-species task aimed to divert participants’ attention away from the repetitive auditory stimuli using visual stimuli in the form of a nature documentary. Additionally, the counting tasks served as a quality control measure, excluding participants whose answer substantially differed from the correct answer. Conversely, the inclusion of both short and long SOI measured adaptation lifetime using the amplitude ratio (explained below in Data Analysis).\r\n Figure 1. A visual representation of the study’s two-by-two factorial design, encompassing four distinct conditions: Active with Short SOI (1.7s), Passive with Short SOI (1.7s), Active with Long SOI (3.4s), and Passive with Long SOI (3.4s).\r\nProcedure\r\nEEG was used as the method of data collection. The Enobio NIC2 suite recorded EEG data, using three dry electrodes (Fpz, Cz, and Fz) to capture neuroelectrical activity in the auditory cortex (Neuroelectrics, n.d.). Data recording was conducted in a sound-attenuated laboratory. The entire experiment lasted approximately 60 minutes, which included a 20-minute preparation period.\r\nBefore the experiment, participants were sent an information sheet online and completed a consent form upon arrival. They were then fitted with an electrode cap and headphones, and instructed to avoid excessive movement during recording to minimise muscle artifacts. When recording was ongoing, participants were verbally given instructions at the start of each condition, and they were asked about their answers to the counting tasks after each condition. Short breaks were allowed when transitioning between conditions. After the experiment, participants were inquired about their age and gender, and received a verbal and written debrief regarding the true purpose of the study.\r\nData Analysis\r\nWe conducted a priori power analyses using G*Power 3.1. to determine the required sample size for testing the two hypotheses (Faul et al., 2007). For the preliminary investigation, results indicated that the required sample size to achieve 80% power for detecting a medium effect, at a significance criterion of α = .05 was N = 36 for a two-way ANOVA. For the primary investigation, results indicated that the required sample size to achieve 80% power for detecting a medium effect, at a significance criterion of α = .05 was N = 34 for a paired-sample t-test. Our recruitment target of 36 participants was based on the larger of the two required sample sizes.\r\nIn data preprocessing, we discarded the first few trials from each condition to minimise initial variability in orienting and habituation effects, and excluded any unidentifiable N1 responses.\r\nMeasuring the N1 amplitude is essential for estimating adaptation lifetime and conducting the planned data analysis. There are three methods available - N1, N1-P2, and mean voltage displacement. Notably, baseline correction was performed as a standard initial procedure, addressing a baseline that extended over 100 milliseconds within this experiment. The first method identifies and measures the N1 amplitude as the point of maximum negativity (Marton et al., 2018). The second method measures the peak-to-peak amplitude difference between N1 and P2, as it captures the relationship between the two and avoids the problem of a noisy baseline by not depending on the pre-stimulus baseline (Al-Abduljawad et al., 2008; Scaife et al., 2006). The third method estimates the mean voltage displacement (absolute amplitude value) over a specific time frame, particularly useful when the N1 component is difficult to identify, or the stimulus onset is ambiguous (Hoehne et al., 2020; Komssi et al., 2004). All three methods were employed to conduct a more comprehensive data analysis, given that consistent findings across different methods increase the reliability of results and inconsistencies can guide further investigation.\r\nIn the traditional approach for estimating adaptation lifetime, one uses multiple stimulus blocks, each featuring varying SOIs ranging from 0.5 to 10 seconds. The ERP is derived separately for each stimulus block, and notably, the peak N1 amplitude is plotted as a monotonically increasing function of SOI. This relationship between the N1 amplitude and the SOI can be described as an exponentially saturating function, represented by the model equation A(1-e-(t-to)/τ), where A (amplitude), τ (time constant), and to (time origin) represent fitting parameters (Lü et al., 1992). Graphically, one fits the exponentially saturating curve to the measured N1 amplitudes. Here, the fitting parameter τ characterizes the steepness of the curve in seconds. τ signifies the SOI at which the amplitude curve reaches 66% of its way towards the saturation limit, indicating the lifetime of adaptation. However, this method is time-consuming and difficult for participants, insofar as boredom-induced mind wandering may confound the effects of attentional focus (Eastwood et al., 2012; Meier et al., 2023).\r\nAn alternative approach of amplitude ratio only used two stimulus blocks with contrasting SOIs. By graphically plotting the amplitude ratio of a short SOI against a long SOI over a range of τ values (measured in seconds), it shows that the ratio is a monotonically increasing function of τ. Although this ratio-to-τ relationship is not strictly linear, it can be used to estimate the adaptation lifetime rather than the conventional time constant, given that the ratio increases as τ increases. In practical terms, both SOI conditions produced a clear difference in amplitude. The short SOI of 1.7 seconds ensures a distinct ERP with an observable N1 component (if the SOI is less than 300 milliseconds, it would render the N1 response too minute and difficult to observe), while the long SOI of 3.4 seconds brings the N1 amplitude closer to its saturation limit. By shortening the experiment duration, this ‘dimensionless’ measure addressed the limitations of the traditional approach without significantly compromising estimation accuracy.\r\nTwo-way ANOVAs were conducted to assess how the N1 amplitude is influenced by attentional focus (presence or absence) on repetitive auditory stimuli and SOIs (short or long).\r\nPaired samples t-tests were conducted to assess if the presence or absence of attentional focus on repetitive auditory stimuli significantly affects adaptation lifetime (calculated via amplitude ratio).\r\n"]]]],["element",{"elementId":"45"},["name","Publisher"],["description","An entity responsible for making the resource available"],["elementTextContainer",["elementText",{"elementTextId":"3838"},["text","Lancaster University "]]]],["element",{"elementId":"42"},["name","Format"],["description","The file format, physical medium, or dimensions of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3839"},["text","Data/SPSS.sav"]]]],["element",{"elementId":"43"},["name","Identifier"],["description","An unambiguous reference to the resource within a given context"],["elementTextContainer",["elementText",{"elementTextId":"3840"},["text","Ho2023"]]]],["element",{"elementId":"37"},["name","Contributor"],["description","An entity responsible for making contributions to the resource"],["elementTextContainer",["elementText",{"elementTextId":"3841"},["text","Sharon Boyd"]]]],["element",{"elementId":"47"},["name","Rights"],["description","Information about rights held in and over the resource"],["elementTextContainer",["elementText",{"elementTextId":"3842"},["text","Open"]]]],["element",{"elementId":"46"},["name","Relation"],["description","A related resource"],["elementTextContainer",["elementText",{"elementTextId":"3843"},["text","None"]]]],["element",{"elementId":"44"},["name","Language"],["description","A language of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3844"},["text","English"]]]],["element",{"elementId":"51"},["name","Type"],["description","The nature or genre of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3845"},["text","Data"]]]],["element",{"elementId":"38"},["name","Coverage"],["description","The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant"],["elementTextContainer",["elementText",{"elementTextId":"3846"},["text","LA1 4YF"]]]]]],["elementSet",{"elementSetId":"4"},["name","LUSTRE"],["description","Adds LUSTRE specific project information"],["elementContainer",["element",{"elementId":"52"},["name","Supervisor"],["description","Name of the project supervisor"],["elementTextContainer",["elementText",{"elementTextId":"3847"},["text","Patrick May"]]]],["element",{"elementId":"53"},["name","Project Level"],["description","Project levels should be entered as UG or MSC"],["elementTextContainer",["elementText",{"elementTextId":"3848"},["text","MSc"]]]],["element",{"elementId":"54"},["name","Topic"],["description","Should contain the sub-category of Psychology the project falls under"],["elementTextContainer",["elementText",{"elementTextId":"3849"},["text","Neuropsychology"]]]],["element",{"elementId":"56"},["name","Sample Size"],["description"],["elementTextContainer",["elementText",{"elementTextId":"3850"},["text","Participants: 26\r\nExcluded Participants: 16\r\nFinal Sample: 10 Participants"]]]],["element",{"elementId":"55"},["name","Statistical Analysis Type"],["description","The type of statistical analysis used in the project"],["elementTextContainer",["elementText",{"elementTextId":"3851"},["text","ANOVA, t-test"]]]]]]]],["item",{"itemId":"187","public":"1","featured":"0"},["fileContainer",["file",{"fileId":"211"},["src","https://www.johnntowse.com/LUSTRE/files/original/3f375427b3cd3cd552632ac865895843.pdf"],["authentication","1414b72894a9a0b026784d7012d88fd3"]]],["collection",{"collectionId":"3"},["elementSetContainer",["elementSet",{"elementSetId":"1"},["name","Dublin Core"],["description","The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/."],["elementContainer",["element",{"elementId":"50"},["name","Title"],["description","A name given to the resource"],["elementTextContainer",["elementText",{"elementTextId":"181"},["text","EEG"]]]],["element",{"elementId":"41"},["name","Description"],["description","An account of the resource"],["elementTextContainer",["elementText",{"elementTextId":"182"},["text","Electroencephalography (EEG) is a method for monitoring electrical activity in the brain. It uses electrodes placed on or below the scalp to record activity with coarse spatial but high temporal resolution"]]]]]]]],["itemType",{"itemTypeId":"14"},["name","Dataset"],["description","Data encoded in a defined structure. Examples include lists, tables, and databases. A dataset may be useful for direct machine processing."]],["elementSetContainer",["elementSet",{"elementSetId":"1"},["name","Dublin Core"],["description","The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/."],["elementContainer",["element",{"elementId":"50"},["name","Title"],["description","A name given to the resource"],["elementTextContainer",["elementText",{"elementTextId":"3732"},["text","The Effect of Repetitive Headers on Acute Vestibular, Neural, Cognitive and Auditory Function in Football Players"]]]],["element",{"elementId":"39"},["name","Creator"],["description","An entity primarily responsible for making the resource"],["elementTextContainer",["elementText",{"elementTextId":"3733"},["text","Jessica Andrew"]]]],["element",{"elementId":"40"},["name","Date"],["description","A point or period of time associated with an event in the lifecycle of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3734"},["text","September 5th,2023"]]]],["element",{"elementId":"41"},["name","Description"],["description","An account of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3735"},["text","The potential long-term consequences of repetitive sub-concussive head impacts, particularly from heading in football, have raised concerns about their association with neurodegenerative diseases in ex-professional football players. Recent research suggests that the accumulative nature of heading in football may lead to subtle brain changes, ultimately contributing to Chronic Traumatic Encephalopathy. This study aimed to investigate the immediate short-term effects of repeated headers in football on brain function. Seventeen football players completed a total of five high-force linear headers, one header every 2-minutes, imitating corner clearance headers, positioned 32 meters away from a ball launching machine. Four neurophysiological assessments were reported pre- and post-heading exercise: 1) vestibular evaluation for balance and sway changes, 2) neural assessment for resting brain activity changes, 3) cognitive tests measuring memory, attention and reaction time, 4) auditory assessment to assess any auditory processing changes. Paired-samples t-tests and Wilcoxon’s signed rank tests found no significant changes in pre-to-post heading exercise scores in any measurements. These findings warrant further investigation to determine whether the measures used were sensitive enough to detect subtle sub-concussive changes. Or, whether findings indicate a safe maximum number, specific to this type of header, has been established and this frequency does not pose any additional risks to footballers’ brain function. This study contributes to the ongoing research surrounding player safety in football and the immediate short-term effects of repetitive sub-concussive head impacts.\r\n"]]]],["element",{"elementId":"49"},["name","Subject"],["description","The topic of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3736"},["text","Repetitive Sub-concussion, Football Heading, Neurocognitive Performance"]]]],["element",{"elementId":"48"},["name","Source"],["description","A related resource from which the described resource is derived"],["elementTextContainer",["elementText",{"elementTextId":"3737"},["text","Method\r\nParticipants\r\nA power analysis for Analysis of Variance was conducted to determine the sample size needed for this study with an 80% power level, which identified a minimum of 40 participants to achieve a medium effect size of f=0.25, α=.001. This study did not collect a full sample and therefore is underpowered, as there are only a total of 17 participants (mean age=20.35). Participants were either academy players from Burnley Football Club or Lancaster University’s football team and were required to be male aged between 18 and 30- years with no history of concussion within the last month. This ensured variability between participants was minimal and excluding individuals with a recent history of concussion will mitigate potential confounding effects and isolate acute sub-concussive effects of heading, meaning this study will better attribute any observed effects to the specific act of heading rather than to prior injuries. Prior to volunteering, participants gave full consent and completed a modified version of the Physical Activity Readiness Questionnaire (PAR-Q), which is designed to measure participants readiness to participate in exercise or physical activity. See Appendix A for questionnaire. The purpose of the PAR-Q was to identify any potential underlying health concerns that may become an issue when participating. Additionally, participants completed a demographic questionnaire which was used to collect information about characteristics of the sample and highlighted whether participants had recently been concussed. See Appendix B for questionnaire. If any health concerns emerged during the completion of either questionnaire, participants were unable to continue with participation.\r\nMaterials\r\nParticipants were tested using a test battery comprised from four elements detailed below.\r\nPROTXX.\r\nVestibular sway was measured using a wearable inertial measurement unit (IMU), called PROTXX. IMU is an electronic device designed to measures and report an individual’s orientation, velocity and gravitational forces (Powell et al., 2022). The IMU includes an accelerometer with three axis, X, Y and Z. The X-axis measures front-back acceleration, Y- axis measures vertical acceleration, and Z-axis measures left-right acceleration. For each of the three axes (x, y and z), during each 60 second test, data is recorded at a sampling rate of 100Hz and generates a total of 12,000 samples. Samples are filtered, meaning PROTXX eliminates gravitational bias and drift by using a high pass filter with a .04Hz cut-off frequency. An overall average is taken for each axis to compute one score for each of the four measures, 1) eyes open, 2) eyes closed 3) a ratio of the first two scores and 4) average power. It is also thought that the average power, calculated by adding the eyes closed and eyes open scores together, and divided by 2, can support a more objective way to clinically diagnose concussion, rather than the single tests alone (Ralston et al., 2020).\r\nEEG Acquisition and Pre-Processing\r\nNeural function was measured using EEG, Enobio 8 5G wireless device (Neuroelectrics, Cambridge, MA, USA). Participants wore a Neoprene headband to collect data from the frontal part of the head only, as this is where participants will later be instructed to header the ball. The Neoprene headband offers predefined positions for seven channels (F7, AF8. Fp1, Fpz, Fp2, AF8. F8) used to record EEG data and is based on the 10-10 international system (Jurcak et al., 2007). Figure 1 is a schematic of electrode location sites on the forehead. Participants wore an ear clip on their right ear with reference DRL/CMS electrodes. EEG data was initially visualised at a sampling rate of 500Hz and the line noise filter at 50Hz. Sticktrode pre-gelled self-adhesive electrodes were used and placed under the gaps of the Neoprene headband.The Necbox, is the core of the Enobio system, and is wirelessly connected to a laptop using NIC software (Neuroelectrics, Barcelona, Spain). Before any analysis, recorded EEG signals were coded and pre-processed in EEGLAB, a MATLAB toolbox (See Appendix C for EEGLAB Script) (Mathworks, Natick, MA, USA) (Delorme & Makeig, 2004). This is to ensure that data is in a suitable format and quality for analysis is reliable. Signals were downsampled to 256Hz, re-referenced to the average of all channels, and two types of filtering were applied to EEG data, high-pass (0.1Hz) and low-pass (40Hz) filtering. Independent Component Analysis was then applied to the pre-processed EEG data using a threshold of 0.8. This step was added to identify and remove any eye blinks, heart and muscle artifacts with 80% certainty (Chang et al., 2020). Components that have a score between 0.8 and 1 for artifacts are flagged for potential rejection and removed from EEG data.\r\nNeural activity pre-and post-heading exercise were analysed using power spectral density analysis (PSD). PSD analysis is a method used to analyse frequency components present in a signal. To conduct a PSD analysis, this study used the code spectopo() function within EEGLAB. The average power of EEG frequency bands was calculated for each of the seven electrodes used in this study. The frequency bands were separated in the following way: theta (4-8Hz), alpha (8-12Hz), beta (12-30Hz) and gamma (30-40Hz) (Harris & Myers, 2023; Munia et al., 2017).\r\nImPACT Quick Test\r\nImPACT Quick Test measures different areas of cognitive function using five subtests that contribute to three overall composite scores used within this study’s analysis: Motor Speed, Memory, and Attention Tracker. The five subtests used to measure the participants cognitive abilities are:\r\n1. Symbol Match – Reaction Time Subtest. The first subtest was a symbol match test which measured reaction time. Participants had to match a series of shapes with a specific number and the average time taken to complete all trials was recorded. (Figure 2a)\r\n2. Symbol Match – Memory Subtest. This symbol match test also measured memory and asked participants to recall the number-symbol pairs and remember which symbol was matched up with which number. The resulting score is the percentage of correctly recalled number-symbol pairs across the trials. (Figure 2b)\r\n3. Three Letter Memory – Speed Subtest. The participant is initially given three consonants. Participants are then given a computer-randomised 5x5 number grid and asked to count backwards from 25. The result is how long it takes the participant to count backwards from 25 to 1. This subtest provides a measure of speed, but also serves as an interference task for the next subtest. (Figure 2c)\r\n4. Three Letter Memory – Memory Subtest. This subtest measures the participants memory and recall. It provided a measure of memory and tested how well the participants could recall the three consonants after completing the computer-randomised 5x5 number grid interference task. (Figure 2d)\r\n5. Attention Tracker – Reaction Time and Attention Subtest. This subtest is comprised of three separate tasks and involves a circle that moves in the shape of a square, figure 8 and a sporadic/random pattern across the screen. The participant is asked to tap the circle when it changes from red to green at various points during its movement. This subtest provides results for reaction time and how fast the participant can react to the colour change and how well the participant can keep their attention sustained on the moving circle. (Figure 2e)\r\nDigits in Noise Test (DiN)\r\nThe final testing measure used within this study was an online DiN test to measure participant’s auditory function. The DiN task is written in Javascript and hosted as a web- application on a Google Cloud Platform. Participants remained seated for this measure and listened to a British female voice who said three digits in a random order that are embedded into speech-shaped background noise (Smits et al., 2004). Stimuli was presented diotically in a quiet environment through supplied wired overhead SteelSeries 5Hv2 headphones. Signal- to-noise ratio (SNR) is a measure used to quantify strength of a desired signal relative to background noise level. A flexible approach called an adaptive 1-up, 1-down psychophysical method was employed. When a participant recalled the three digits correctly, SNR decreased, and when participants recalled the digits incorrectly, SNR increased. The DiN test began with a SNR of 0dB. As the test progressed, the changes in difficulty, known as step sizes, decreased from 5 to 2 dB after 3 reversals. Then after 3 more reversals, step sizes reduced even more to 0.5dB. A reversal refers to a change in direction, therefore the difficulty level is adjusted in the opposite direction. The test concluded after a total of 10 reversals and the final five SNR were recorded and an average was created, to calculate the participant’s speech in noise threshold. This threshold represents the level of background noise at which participants correctly identify the digits spoken to them. Football Heading\r\nWithin this study, participants received headers by a ball launching machine (Ball Launcher Pro Trainer, Ball Launcher). Participants completed five high-force linear headers at 35 yards from the ball launching machine at a ball speed of 50mph, the speed of the ball is regarded as below the average corner kick for collegiate-level players, which helps reduce the likelihood of injury and discomfort to players (Elbin et al., 2015; Tierney et al., 2021). This exercise is designed to mimic heading during football matches, specifically a clearance header from a corner (Figure 3). This ball launcher allowed for each of the headers to be consistent when measuring the effects of heading in football. The football used in this study was size 5, inflated to the FA standards of 8.6-15.6 PSI (The Football Association, 2023).\r\nProcedure\r\nA chronological schematic representation of the experimental procedure has been provided below (Figure 4).\r\nPlayers at Burnley Football Club were contacted via their club’s representative and Lancaster University players were emailed directly. Upon arrival, participants were informed that the study will take around one hour to complete and asked to read the participant information sheet to ensure they fully understood the requirements before completing the consent, PAR-Q and demographic form. Participants height and weight was taken on the day, meaning that the demographic questionnaire will be filled in accurately. These forms were screened by the researcher(s) to ensure eligibility. Once completed, participants were first tested using PROTXX sensor. Participants were asked whether they experience any skin irritation or sensitivities due to prolonged adhesive contact, for example when using plasters. If there were no known adhesive-related reactions, PROTXX sensor was attached to the right mastoid using a disposable medical adhesive patch (figure 5). However, if participants did have adhesive-related reactions, PROTXX sensor was placed into a headband, and positioned in the same location (figure 6).\r\nParticipants were instructed to stand still, in an upright relaxed position with feet hips width apart and arms by their side whilst maintaining a straight, fixed gaze, three meters away from a specific target. Participants were instructed not to talk, chew gum, turn their head, fidget or move while the test is in progress. A smartphone app (protxxclinic; Version 1.0 build 13), connects to PROTXX via Bluetooth to run the tests and collect data. Participants completed two 60 second trials; eyes open and eyes closed. The app is used to start the test and participants are made aware of an audible countdown. One researcher stood by the participant to ensure no apprehension of falling during the eyes closed trial. The app sounded a tone signifying the test was 10-seconds away from finishing. Participants were instructed not to move until tests are completed and researchers had informed them, they can relax. If any anomalous participant movement was observed during the testing, said test data was excluded from analysis.\r\nThe second testing measure completed was EEG. Participants were seated for this measure and prior to setting up EEG, they were asked to wipe their foreheads with an alcohol wipe to reduce the impedance. Participants wore a Neoprene headband across their forehead with seven pre-gelled adhesive electrodes placed on bare skin located at each channel site and the reference channels were linked to their right ear (figure 7).\r\nElectrode placement was completed, then connected via Bluetooth to a desktop app. The researcher(s) instructed participants to blink rapidly several times to create distinct electrical patterns on EEG recordings. This procedure is known as artifact-inducing task and is used to verify the quality of EEG readings (Grosselin et al., 2019). Participants were asked to sit in a comfortable position with eyes closed and 5-minutes of resting state EEG activity was recorded. A quiet environment was used, with minimal foot traffic, to reduce background noise and lessened potential of any auditory artifacts.\r\nThe third testing measure completed was ImPACT Quick Test. Participants remained seated for this measure and completed the assessment tool on an iPad in a quiet environment to remove distractions. The iPad was placed on a table in front of the participant who was instructed not to hold the iPad in their hands (Figure 8). The test was taken in one sitting and took participants between 5-7 minutes to complete.\r\nThe final testing measure participants completed was DiN. This measure required participants to remain seated in the quiet environment and wear provided overhead- headphones, that were plugged into the iPad (Figure 9). Before the test began, some music played through the headphones and participants were asked to find a volume level that was comfortable for them and were instructed to not change once selected. Participants were informed that this measure will vary in difficulty, and to guess the digits if they were unable to identify them. There was an opportunity to have a practice trial at this measure, so participants were familiar with the task and response procedure before the measure began. Participants would input three digits that they heard or guessed on the iPad’s keypad displayed. Again, this test was to be completed in one sitting and took no more than 3- minutes to complete.\r\nAfter all baseline assessments were complete, participants moved on to the heading exercise, which was conducted in an indoor open space. The primary objective of this exercise was to execute five consecutive linear high-force headers within a timeframe of 10- minutes, giving participants 2-minutes rest between each header. Before commencing the heading exercise, participants received a briefing to prepare them. They were informed about their designated position, situated 35 yards away from the ball launching machine, replicating the distance of a typical corner kick in real-game scenarios. The ball would be launched at a velocity of 50mph from a ball launching machine, ensuring consistency. To optimise their heading technique, participants were encouraged to aim for frontal contact and direct the ball back in a linear trajectory towards the ball launching machine and were allowed to take a single step and execute a jump into the header (to replicate real-life situations). Additionally, a secondary researcher positioned further back from the participant was responsible for retrieving any missed headers, thereby sparing participants unnecessary energy expenditure. To familiarise participants with the dynamics and to help maximise their performance during this heading task, participants were acclimatised to the ball’s trajectory, observing several ball launches from the side-line and standing in their designed position before initiating any heading attempts. This also ensured that participants were comfortable with the ball speed.\r\nParticipants immediately completed the test battery again to obtain their post-heading scores, which were compared to evaluate the effect of headers on various test battery components. To close the study, participants were given a debrief sheet, and given a further opportunity to ask questions or raise concerns.\r\nStatistical Analysis\r\nData pre- and post-heading were evaluated using paired-samples t-tests. The specific data used to input into the analyses was the independent variable, the point at which participants completed the test battery, pre-post heading exercise. The dependent variables\r\nconsisted of data collected from the different measures: PROTXX; using individual eyes open and closed condition sway power scores, in addition to ratio and average power of these conditions, EEG; PSD for the four frequency bands, (alpha, beta, theta and gamma) were averaged across each seven electrodes for each participant, ImPACT; overall composite scores for each cognitive domain (motor speed, memory and attention) and DIN; SNR thresholds. The paired-samples t-test is specifically designed to compare the means or averages of two related groups. These analyses test for immediate short-term effects that may occur after RSHI. Data was tested for normality using Shapiro-Wilks’ test (Shapiro & Wilk, 1965). This step is crucial to verify whether the data meets parametric assumption of a normal distribution before proceeding with further analyses. Analyses were performed using statistical software R Studio. See Appendix D for R Studio Script."]]]],["element",{"elementId":"45"},["name","Publisher"],["description","An entity responsible for making the resource available"],["elementTextContainer",["elementText",{"elementTextId":"3738"},["text","Lancaster University"]]]],["element",{"elementId":"42"},["name","Format"],["description","The file format, physical medium, or dimensions of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3739"},["text","Excel.csv(\"Linear Heading Study Data.xlsx\")\r\nr_file.R(\"Dissertation_Masters.R\")"]]]],["element",{"elementId":"43"},["name","Identifier"],["description","An unambiguous reference to the resource within a given context"],["elementTextContainer",["elementText",{"elementTextId":"3740"},["text"," Andrew2023"]]]],["element",{"elementId":"37"},["name","Contributor"],["description","An entity responsible for making contributions to the resource"],["elementTextContainer",["elementText",{"elementTextId":"3741"},["text","Niko Liu ,Anusha Sandeep, David Racovita"]]]],["element",{"elementId":"47"},["name","Rights"],["description","Information about rights held in and over the resource"],["elementTextContainer",["elementText",{"elementTextId":"3742"},["text","'Open'"]]]],["element",{"elementId":"46"},["name","Relation"],["description","A related resource"],["elementTextContainer",["elementText",{"elementTextId":"3743"},["text","N/A"]]]],["element",{"elementId":"44"},["name","Language"],["description","A language of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3744"},["text","English"]]]],["element",{"elementId":"51"},["name","Type"],["description","The nature or genre of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3745"},["text","Data"]]]],["element",{"elementId":"38"},["name","Coverage"],["description","The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant"],["elementTextContainer",["elementText",{"elementTextId":"3746"},["text","LA20PF"]]]]]],["elementSet",{"elementSetId":"4"},["name","LUSTRE"],["description","Adds LUSTRE specific project information"],["elementContainer",["element",{"elementId":"52"},["name","Supervisor"],["description","Name of the project supervisor"],["elementTextContainer",["elementText",{"elementTextId":"3787"},["text","Dr Helen Nuttall"]]]],["element",{"elementId":"53"},["name","Project Level"],["description","Project levels should be entered as UG or MSC"],["elementTextContainer",["elementText",{"elementTextId":"3788"},["text","Masters"]]]],["element",{"elementId":"54"},["name","Topic"],["description","Should contain the sub-category of Psychology the project falls under"],["elementTextContainer",["elementText",{"elementTextId":"3789"},["text","Neuropsychology"]]]],["element",{"elementId":"56"},["name","Sample Size"],["description"],["elementTextContainer",["elementText",{"elementTextId":"3790"},["text","17 participants"]]]],["element",{"elementId":"55"},["name","Statistical Analysis Type"],["description","The type of statistical analysis used in the project"],["elementTextContainer",["elementText",{"elementTextId":"3791"},["text","T-Test\r\nOther"]]]]]]]],["item",{"itemId":"114","public":"1","featured":"0"},["collection",{"collectionId":"3"},["elementSetContainer",["elementSet",{"elementSetId":"1"},["name","Dublin Core"],["description","The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/."],["elementContainer",["element",{"elementId":"50"},["name","Title"],["description","A name given to the resource"],["elementTextContainer",["elementText",{"elementTextId":"181"},["text","EEG"]]]],["element",{"elementId":"41"},["name","Description"],["description","An account of the resource"],["elementTextContainer",["elementText",{"elementTextId":"182"},["text","Electroencephalography (EEG) is a method for monitoring electrical activity in the brain. It uses electrodes placed on or below the scalp to record activity with coarse spatial but high temporal resolution"]]]]]]]],["elementSetContainer",["elementSet",{"elementSetId":"1"},["name","Dublin Core"],["description","The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/."],["elementContainer",["element",{"elementId":"50"},["name","Title"],["description","A name given to the resource"],["elementTextContainer",["elementText",{"elementTextId":"2491"},["text","Effect of Attention and Noise on Echoic Memory as Indexed by the N1-Adaptation. "]]]],["element",{"elementId":"39"},["name","Creator"],["description","An entity primarily responsible for making the resource"],["elementTextContainer",["elementText",{"elementTextId":"2492"},["text","Ekenedilichukwu Tonia Osakwe"]]]],["element",{"elementId":"40"},["name","Date"],["description","A point or period of time associated with an event in the lifecycle of the resource"],["elementTextContainer",["elementText",{"elementTextId":"2493"},["text","08.09.2021"]]]],["element",{"elementId":"41"},["name","Description"],["description","An account of the resource"],["elementTextContainer",["elementText",{"elementTextId":"2494"},["text","There are numerous studies that support the notion that echoic memory is indexed by the adaptation of the N1 peak in auditory event related potentials (ERPs). Although the number research on the effects of parameters like noise and attention on the amplitude of the N1 is immense, to date there are no studies on the effect of these parameters on the adaptation of the N1. Here, I investigated the effect of noise and attention on the adaptation of N1, P2 and N1-P2. Secondary analysis was conducted on data collected from 33 participants in three conditions:  passive recording condition (participant listen passively to stimulus while staring at a fixation cross); attention/oddball conditions (participant were task with counting the deviating tones); and noise condition where the tones are presented in white noise. Within each condition, two Stimulus onset intervals (SOI): 1.7 s and 3.5 were used in separate stimulus blocks and the ratio R = M1.7s / M3.5s was used as a dimensionless measure of adaptation. My results found no significant effect of noise an attention on the amplitudes and adaption of the N1, P2 and N1-P2. I propose that the lack of effect on the adaption of the ERPS might be due to noise and attention having a scaling effect on all of the amplitudes equally so that adaption lifetime is not affected. As this is the first study of its kind, further research will be needed to gain a better understanding of how adaptation is affected by these two factors. "]]]],["element",{"elementId":"49"},["name","Subject"],["description","The topic of the resource"],["elementTextContainer",["elementText",{"elementTextId":"2495"},["text","Attention, Noise, N1-adaptation, auditory sensory memory"]]]],["element",{"elementId":"48"},["name","Source"],["description","A related resource from which the described resource is derived"],["elementTextContainer",["elementText",{"elementTextId":"2496"},["text","Participants\r\nThis project carries out secondary analysis on data from an EEG experiment with 33 human participants. The data  was received from supervisor, Patrick May.  The participants were all adult undergraduate and post graduate students at Lancaster University, with no self-reported hearing loss or neurological disorder. The experiment was approved by the research ethics procedures of the Department of Psychology, Lancaster University, and the participants provided written consent before the experiment began. \r\n\r\nEquipment and Procedure for EEG measurements\r\nThree dry electrodes were attached at locations: Fpz, Fz, Cz. Reference and ground electrodes were attached to the right ear lobe. For this report, only the data acquired from the Fz location was used as this is the channel that recorded the best ERPs for all the participants. The participants were directed to passively listen to stimuli while staring at a fixation cross and moving and blinking as little as possible. The stimuli comprised of 500-Hz pure tones with a duration of 100ms, including 10-ms linear onset and offset ramps. The stimuli were presented in blocks of 100 isochronous stimuli. The stimuli were presented binaurally via Sennheiser headphones using laboratory laptop and MATLAB interfaced with the Enobio EEG device in a soundproof chamber. Data was collected in three conditions: baseline passive recording condition (participant listen passively to stimulus while staring at a fixation cross); attention/oddball conditions (participant were task with counting the deviating tones); and noise condition where the tones are presented in white noise. Withing each condition, two Stimulus onset interval (SOI): 1.7 s and 3.5 were used in separate stimulus blocks. The order of experiments were randomised across the participants. \r\nData Analysis\r\nThe data was passband filtered at 1-30 Hz and sectioned into epochs of single trial data. To remove artefacts (e.g., due to blinking) 15% of epochs with the largest absolute amplitudes were removed. Single trial epochs was then averaged to reveal the ERP. The average ERP in a 100ms time window immediately preceding stimulus onset was calculated and subtracted from the whole ERP (baseline correction). The N1 is not the only peak that shows adaptation in auditory ERPS. Although many of the research on adaption is focused on the N1 peak, different researchers have looked at other auditory ERP peaks in relation to adaptations such as the P2 and P3 peaks. In fact, Lanting et al. (2013)  found that the P2 was more very strongly affected by adaption than the N1. In addition, the peak-to-peak difference between the N1 and the P2 has been previously used to estimate adaptation in several studies as it provides a more reliable measure of activity in auditory cortex because as it has the advantage of not being dependent on the baseline activity which can be noisy (Lanting et al., 2013; Lavoie et al., 2008; Muller-Gass et al., 2008). Because of this, both the N1 and the P2 peaks were identified - the N1 was identified as the peak negativity at around 100ms and P2 peak positivity at around 200ms. The peak-to-peak difference between the N1 and the P2  was calculated and the N1 and P2 amplitude as well as the difference between the N1 and P2 amplitude was used to estimate the lifetime of adaptation. Statistical data analysis was conducted using Analysis of Variance (ANOVA). Specifically, three one-way (condition) and three two-way (SOI x condition) repeated measures ANOVAs was conducted of the N1, P2 and the difference between the N1 and P2 amplitudes and amplitude ratios respectively. \r\n\r\nCalculating the lifetime of adaptation (τ)\r\nThe recovery time constant for adaptation is usually calculated by fitting an exponentially saturating function to peak amplitudes plotted across SOIs (Lu et al., 1992). This curve is characterized by  as well as by two other fitting parameters: asymptotic magnitude and crossing point on SOI axis. The parameter  determines the steepness of the magnitude curve: the smaller its value, the quicker the curve approaches the asymptote (i.e., levels out) as SOI is increased. The SOIs where this levelling out has occurred represent stimulation where the silent period between two consecutive stimuli is large enough for adaptation to have died away. Therefore,  expresses the lifetime of adaptation: with low values, the curve levels out to its maximum value quicker; with high values, the amplitude rises slower as a function of SOI, meaning that adaptation is strongly present in a larger range of SOIs.\r\nFor fitting the exponential function reliably, a large number of SOIs should be employed, and the largest SOI should measure approximately 10s to ensure that adaptation has died away. Coupled with the requirements of data quality (large number of stimulus repetitions), this means long measurement times. In this experiment, this was bypassed by noting that the ratio between the magnitudes measured at two different SOIs is proportional to . Expressing the magnitudes of the brain responses measured at SOIs 1.7 s and 3.5 s by M1.7s, and M3.5s, respectively, the ratio R = M1.7s / M3.5s was used as a dimensionless measure of  and adaptation lifetime. The smaller R is, the shorter adaptation lifetime is. R was calculated separately for each participant for each of the experimental conditions and for each SOI. In addition, R was also calculated separately for the N1 and P2 peaks as well as the difference between these peaks. Note that the actual adaptation lifetime cannot be estimated by the use of this method.\r\n\r\nResults\r\n18 participants’ data did not show identifiable ERP responses and were thus discarded from analysis. The ERPs obtained from the final sample of 15 were plotted as shown in Figure 1 for each participant. The means and standard deviations were then calculated for the identified N1, P2 and the difference between the N1 and P2 for each SOI and condition as shown in Table 1. Seeing as there is such a large variability across the conditions, it is predictable that no statistical differences were found by the ANOVA. \r\n"]]]],["element",{"elementId":"45"},["name","Publisher"],["description","An entity responsible for making the resource available"],["elementTextContainer",["elementText",{"elementTextId":"2497"},["text","Lancaster University "]]]],["element",{"elementId":"42"},["name","Format"],["description","The file format, physical medium, or dimensions of the resource"],["elementTextContainer",["elementText",{"elementTextId":"2498"},["text","data/r.csv\r\n"]]]],["element",{"elementId":"43"},["name","Identifier"],["description","An unambiguous reference to the resource within a given context"],["elementTextContainer",["elementText",{"elementTextId":"2499"},["text","Osakwe2021"]]]],["element",{"elementId":"37"},["name","Contributor"],["description","An entity responsible for making contributions to the resource"],["elementTextContainer",["elementText",{"elementTextId":"2500"},["text","Emily Dreyer\r\nPaige Durnall"]]]],["element",{"elementId":"47"},["name","Rights"],["description","Information about rights held in and over the resource"],["elementTextContainer",["elementText",{"elementTextId":"2501"},["text","Open"]]]],["element",{"elementId":"46"},["name","Relation"],["description","A related resource"],["elementTextContainer",["elementText",{"elementTextId":"2502"},["text","None"]]]],["element",{"elementId":"44"},["name","Language"],["description","A language of the resource"],["elementTextContainer",["elementText",{"elementTextId":"2503"},["text","English "]]]],["element",{"elementId":"51"},["name","Type"],["description","The nature or genre of the resource"],["elementTextContainer",["elementText",{"elementTextId":"2504"},["text","Data"]]]],["element",{"elementId":"38"},["name","Coverage"],["description","The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant"],["elementTextContainer",["elementText",{"elementTextId":"2505"},["text","LA1 4YF"]]]]]],["elementSet",{"elementSetId":"4"},["name","LUSTRE"],["description","Adds LUSTRE specific project information"],["elementContainer",["element",{"elementId":"52"},["name","Supervisor"],["description","Name of the project supervisor"],["elementTextContainer",["elementText",{"elementTextId":"2535"},["text","Patrick May"]]]],["element",{"elementId":"53"},["name","Project Level"],["description","Project levels should be entered as UG or MSC"],["elementTextContainer",["elementText",{"elementTextId":"2536"},["text","MSC"]]]],["element",{"elementId":"54"},["name","Topic"],["description","Should contain the sub-category of Psychology the project falls under"],["elementTextContainer",["elementText",{"elementTextId":"2537"},["text","Neuroscience, Neuropsychology"]]]],["element",{"elementId":"56"},["name","Sample Size"],["description"],["elementTextContainer",["elementText",{"elementTextId":"2538"},["text","33 to start, 18 were removed so final number is 15"]]]],["element",{"elementId":"55"},["name","Statistical Analysis Type"],["description","The type of statistical analysis used in the project"],["elementTextContainer",["elementText",{"elementTextId":"2539"},["text","ANOVA"]]]]]]]]]