KOINEU

Balancing Interpretability and Performance in Motor Imagery EEG Classification: A Comparative Study of ANFIS-FBCSP-PSO and EEGNet

모터 이미징 EEG 분류는 BCI 시스템에서 핵심적인 단계입니다. ANFIS FBCSP PSO와 같은 퍼지 추론 접근법은 해석 가능성과 관련하여 장점을 제공합니다. 이는 필터 뱅크 공통 공간 패턴을 사용하여 특징을 추출하고, 입자 군집 최적화를 통해 규칙을 최적화함으로써 투명한 분류를 가능하게 합니다. 반면, EEGNet과 같은 딥 러닝 모델은 원시 EEG 데이터에서 직접 학습하여 더 나은 일반화 능력을 보여줍니다. 이 연구는 두 가지 접근 방식의 장단점을 비교하고, 내부 주체 및 교차 주체 실험을 통해 성능을 평가합니다. 또한,

Balancing Interpretability and Performance in Motor Imagery EEG Classification: A Comparative Study of ANFIS-FBCSP-PSO and EEGNet

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