LSTM Hyperparameter Optimization for an EEG-Based Efficient Emotion Classification in BCI

BCI에서 EEG 기반 효율적인 감정 분류를 위한 LSTM 하이퍼파라미터 최적화

  • ;
  • ;
  • 임창균 (전남대학교 컴퓨터공학전공)
  • Received : 2019.10.26
  • Accepted : 2019.12.15
  • Published : 2019.12.31


Emotion is a psycho-physiological process that plays an important role in human interactions. Affective computing is centered on the development of human-aware artificial intelligence that can understand and regulate emotions. This field of study is also critical as mental diseases such as depression, autism, attention deficit hyperactivity disorder, and game addiction are associated with emotion. Despite the efforts in emotions recognition and emotion detection from nonstationary, detecting emotions from abnormal EEG signals requires sophisticated learning algorithms because they require a high level of abstraction. In this paper, we investigated LSTM hyperparameters for an optimal emotion EEG classification. Results of several experiments are hereby presented. From the results, optimal LSTM hyperparameter configuration was achieved.


Supported by : National Research Foundation of Korea(NRF)


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