Esma Kabasakal, Hilal Özcebe, Umut Ece Arslan
Information Requirements and Sources of Families of Mainstreaming Students Elementary Education Model in Respect of Socioeconomic Development Levels
Florence Nightingale journal of nursing
classification motor imagery mi using electroencephalography eeg plays pivotal role facilitating communication individuals physical limitations braincomputer interface bci systems recent strides attentionbased networks atn showcased remarkable performance eeg signal classification presenting promising alternative conventional convolutional neural networks cnns however cnns extensively analyzed resilience adversarial attacks susceptibility atns comparable scenarios remains largely unexplored paper aims fill gap investigating robustness atns adversarial contexts propose highperforming attentionbased deep learning model specifically designed classifying motor imagery mi brain signals extracted eeg data subsequently conduct thorough series experiments assess various attack strategies targeting atns employed eegbased bci tasks analysis utilizes widely recognized bci competition 2a dataset demonstrate effectiveness attention mechanisms bci endeavors despite achieving commendable classification results terms accuracy 8715 kappa score 08287 findings reveal vulnerability attentionbased models adversarial manipulations accuracy 907 kappa score 021 highlighting imperative bolstering robustness attention architectures eeg classification tasks