2 research outputs found
Towards Real-World BCI: CCSPNet, A Compact Subject-Independent Motor Imagery Framework
A conventional subject-dependent (SD) brain-computer interface (BCI) requires
a complete data-gathering, training, and calibration phase for each user before
it can be used. In recent years, a number of subject-independent (SI) BCIs have
been developed. However, there are many problems preventing them from being
used in real-world BCI applications. A weaker performance compared to the
subject-dependent (SD) approach, and a relatively large model requiring high
computational power are the most important ones. Therefore, a potential
real-world BCI would greatly benefit from a compact low-power
subject-independent BCI framework, ready to be used immediately after the user
puts it on. To move towards this goal, we propose a novel subject-independent
BCI framework named CCSPNet (Convolutional Common Spatial Pattern Network)
trained on the motor imagery (MI) paradigm of a large-scale
electroencephalography (EEG) signals database consisting of 21600 trials for 54
subjects performing two-class hand-movement MI tasks. The proposed framework
applies a wavelet kernel convolutional neural network (WKCNN) and a temporal
convolutional neural network (TCNN) in order to represent and extract the
diverse spectral features of EEG signals. The outputs of the convolutional
layers go through a common spatial pattern (CSP) algorithm for spatial feature
extraction. The number of CSP features is reduced by a dense neural network,
and the final class label is determined by a linear discriminative analysis
(LDA) classifier. The CCSPNet framework evaluation results show that it is
possible to have a low-power compact BCI that achieves both SD and SI
performance comparable to complex and computationally expensive.Comment: 15 pages, 6 figures, 6 tables, 1 algorith