Akima Connelly, Pengcheng Li, Phurin Rangpong, Theerawit Wilaiprasitporn, T. Yagi
{"title":"Effects of Trial-Adjusted Neurofeedback Training on Motor-Imagery Based Brain-Computer Interface Performance","authors":"Akima Connelly, Pengcheng Li, Phurin Rangpong, Theerawit Wilaiprasitporn, T. Yagi","doi":"10.1109/MeMeA57477.2023.10171918","DOIUrl":null,"url":null,"abstract":"Motor imagery (MI) classification based on electroencephalography (EEG) has been extensively studied and recently used more in brain-computer interfaces (BCI). This study uses left and right hand MI tasks for the BCI system. A common obstacle for MI-BCI is the inability of some participants to perform the BCI task, called BCI illiteracy. Various training protocols have been investigated to improve the performance of BCI but are designed with a balanced dataset. Similarly to how people show a bias towards a side (e.g. left or right) for motor execution tasks, it has been seen that participants also show a performance bias in MI tasks as well. To address this MI bias in participants, a novel neurofeedback protocol was designed to adjust the number of trials each condition has. Trials will be adjusted to increase the number of times participants have to perform their weak MI task. This study aims to investigate the overall effect that the trial-adjusted neurofeedback had on participant’s cognitive performance on the MI-BCI system. The effects were investigated through time-frequency and band power analysis. The time-frequency analysis showed improvement in key MI feature and band power analysis results had an improvement on the alpha and beta frequency bands. In the analysis results, trial-adjusted neurofeedback was seen to have an effect on participant’s cognitive performance on the MI-BCI task.","PeriodicalId":191927,"journal":{"name":"2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MeMeA57477.2023.10171918","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Motor imagery (MI) classification based on electroencephalography (EEG) has been extensively studied and recently used more in brain-computer interfaces (BCI). This study uses left and right hand MI tasks for the BCI system. A common obstacle for MI-BCI is the inability of some participants to perform the BCI task, called BCI illiteracy. Various training protocols have been investigated to improve the performance of BCI but are designed with a balanced dataset. Similarly to how people show a bias towards a side (e.g. left or right) for motor execution tasks, it has been seen that participants also show a performance bias in MI tasks as well. To address this MI bias in participants, a novel neurofeedback protocol was designed to adjust the number of trials each condition has. Trials will be adjusted to increase the number of times participants have to perform their weak MI task. This study aims to investigate the overall effect that the trial-adjusted neurofeedback had on participant’s cognitive performance on the MI-BCI system. The effects were investigated through time-frequency and band power analysis. The time-frequency analysis showed improvement in key MI feature and band power analysis results had an improvement on the alpha and beta frequency bands. In the analysis results, trial-adjusted neurofeedback was seen to have an effect on participant’s cognitive performance on the MI-BCI task.