Post-Stroke identification of EEG signals using recurrent neural networks and long short-term memory - Wanodya Sansiagi; Esmeralda Contessa Djamal; Daswara Djajasasmita; Arlisa Wulandari (Universitas Ahmad Dahlan, 2021)
Stroke often causes disability, so patients need rehabilitation for recovery. Therefore, it is necessary to measure its effectiveness. An Electroencephalogram (EEG) can capture the improvement of activity in the brain in stroke rehabilitation. Therefore, the focus is on the identification of several post-rehabilitation conditions. This paper proposed identifying post-stroke EEG signals using Recurrent Neural Networks (RNN) to process sequential data. Memory control in the use of RNN adopted Long Short-Term Memory. Identification was provided out on two classes based on patient condition, particularly