Classification and recognition of gesture EEG signals with Transformer-Based models
Yan Qu, Congsheng Li, Haoyu Jiang · 2024
The fine recognition of gestures based on non-invasive EEG signals is an important tool for restoring motor function in stroke patients. By designing actual hand movements, the long-term electroencephalogram (EEG) signals of the finger motion technique range are also processed while the object data is processed. We constructed models in time and space, and the improved Transformers model achieved good results in ranking and recognition.