Subject-Independent Brain–Computer Interfaces Based on Deep Convolutional Neural Networks

By O-Yeon Kwon, Min-Ho Lee, Cuntai Guan, Seong-Whan Lee. IEEE Transactions on Neural Networks and Learning Systems, 2019

297 citations recorded by the source corpus.

For a brain–computer interface (BCI) system, a calibration procedure is required for each individual user before he/she can use the BCI. This procedure requires approximately 20–30 min to collect enough data to build a reliable decoder. It is, therefore, an interesting topic to build a calibration-free, or subject-independent, BCI. In this article, we construct a large motor imagery (MI)-based electroencephalography (EEG) database and propose a subject-independent framework based on deep convolutional neural networks (CNNs). The database is composed of 54 subjects performing the left- and right-hand MI on two different days, resulting in 21 600 trials for the MI task. In our framework, we formulated the discriminative feature representation as a combination of the spectral–spatial input embedding the diversity of the EEG signals, as well as a feature representation learned from the CNN through a fusion technique that integrates a variety of discriminative brain signal patterns. To generate spectral–spatial inputs, we first consider the discriminative frequency bands in an information-theoretic observation model that measures the power of the features in two classes. From discriminative frequency bands, spectral–spatial inputs that include the unique characteristics of brain signal patterns are generated and then transformed into a covariance matrix as the input to the CNN. In the process of feature representations, spectral–spatial inputs are individually trained through the CNN and then combined by a concatenation fusion technique. In this article, we demonstrate that the classification accuracy of our subject-independent (or calibration-free) model outperforms that of subject-dependent models using various methods [common spatial pattern (CSP), common spatiospectral pattern (CSSP), filter bank CSP (FBCSP), and Bayesian spatio-spectral filter optimization (BSSFO)].

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/d4c3b59b4511c4de93cb681dd0add544d7f77feb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

1. Cuntai Guan

In Cognitive Neuroscience

1. Yi Ding
2. Qiuhao Zeng
3. Su Zhang
4. Neethu Robinson
5. Cuntai Guan
6. Fabien Lotte
7. Dongrui Wu

Citations in the corpus

• TSception: Capturing Temporal Dynamics and Spatial Asymmetry From EEG for Emotion Recognition (2021, methodology): “Recently, deep learning-based methods have shown promising results in BCI domain, such as motor imagery classification [15] [16] [17] [18] [19], emotion recognition [20] [21] [22] [23], and mental-task classification [24] [25] [26].”
• Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review (2021, background): “[73] 20 0–40 N/A EF: CSP CNN (multibranch) 3 9 3 CONV 2 FC 2 OUT ReLU: conv N/A: FC Smax: L-FC”