EMF
BCI MI framework to classifiy brain signals using a multimodal decission making phase, with an addtional differentiation of the signal.
Motor-Imagery-Based Brain Computer Interface using Signal Derivation and Aggregation Functions
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Electroencephalogram (EEG) is a method of recording brain activity using electrophysiological indexes. When the brain is active, a large number of postsynaptic potentials generated synchronously by neurons are formed after summation. It records the changes of electric waves during brain activity and is the overall reflection of the electrophysiological activities of brain nerve cells on the surface of cerebral cortex or scalp. Brain waves originate from the postsynaptic potential of the apical dendrites of pyramidal cells. The formation of synchronous rhythm of EEG is also related to the activity of nonspecific projection system of cortex and thalamus. EEG is the basic theoretical research of brain science. EEG monitoring is widely used in its clinical application.
관련 논문에서 소개한 기법입니다. 이 태스크에서의 평가 여부는 평가 결과에서 확인하세요.
BCI MI framework to classifiy brain signals using a multimodal decission making phase, with an addtional differentiation of the signal.
Motor-Imagery-Based Brain Computer Interface using Signal Derivation and Aggregation Functions
The Enhanced Fusion Framework proposes three different ideas to improve the existing MI-based BCI frameworks. Image source: Fumanal-Idocin et al.
Motor-Imagery-Based Brain Computer Interface using Signal Derivation and Aggregation Functions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.