Deep Learning with Convolutional Neural Networks for Motor Brain-Computer Interfaces based on Stereo-electroencephalography (SEEG)

Xiaolong Wu, Shize Jiang, Guangye Li, Shengjie Liu, Benjamin Metcalfe, Liang Chen, Dingguo Zhang

Research output: Contribution to journalArticlepeer-review

2 Citations (SciVal)
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Objective: Deep learning based on convolutional neural networks (CNN) has achieved success in brain-computer interfaces (BCIs) using scalp electroencephalography (EEG). However, the interpretation of the so-called 'black box' method and its application in stereo-electroencephalography (SEEG)-based BCIs remain largely unknown. Therefore, in this paper, an evaluation is performed on the decoding performance of deep learning methods on SEEG signals. Methods: Thirty epilepsy patients were recruited, and a paradigm including five hand and forearm motion types was designed. Six methods, including filter bank common spatial pattern (FBCSP) and five deep learning methods (EEGNet, shallow and deep CNN, ResNet, and a deep CNN variant named STSCNN), were used to classify the SEEG data. Various experiments were conducted to investigate the effect of windowing, model structure, and the decoding process of ResNet and STSCNN. Results: The average classification accuracy for EEGNet, FBCSP, shallow CNN, deep CNN, STSCNN, and ResNet were 35 ± 6.1%, 38 ± 4.9%, 60 ± 3.9%, 60 ± 3.3%, 61 ± 3.2%, and 63 ± 3.1% respectively. Further analysis of the proposed method demonstrated clear separability between different classes in the spectral domain. Conclusion: ResNet and STSCNN achieved the first- and second-highest decoding accuracy, respectively. The STSCNN demonstrated that an extra spatial convolution layer was beneficial, and the decoding process can be partially interpreted from spatial and spectral perspectives. Significance: This study is the first to investigate the performance of deep learning on SEEG signals. In addition, this paper demonstrated that the so-called 'black-box' method can be partially interpreted.

Original languageEnglish
Pages (from-to)2387-2398
Number of pages12
JournalIEEE Journal of Biomedical and Health Informatics
Issue number5
Early online date6 Feb 2023
Publication statusPublished - 1 May 2023

Bibliographical note

EPSRC New Horizons Grant of UK
10.13039/501100001809-National Natural Science Foundation of China
10.13039/501100002858-China Postdoctoral Science Foundation (Grant Number: 20Z102060158)
Medical & Engineering Cross Foundation of SJTU (Grant Number: AH0200003)


  • Stereo-electroencephalography (SEEG)
  • brain-computer interface (BCI)
  • convolutional neural networks (CNN)
  • deep learning
  • forearm and hand motion

ASJC Scopus subject areas

  • Health Information Management
  • Health Informatics
  • Electrical and Electronic Engineering
  • Computer Science Applications


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