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The EEG signals recorded during Brain Computer Interfaces (BCIs) are naturally represented by multi-way arrays in spatial, temporal, and frequency domains. In order to effectively extract the underlying components from brain activities which correspond to the specific mental state, we propose the higher-order PLS approach to find the latent variables related to the target labels and then make classification...
Single trial electroencephalogram (EEG) classification is essential in developing brain-computer interfaces (BCIs). However, popular classification algorithms, e.g., common spatial patterns (CSP), usually highly depend on the prior neurophysiologic knowledge for noise removing, although this knowledge is not always known in practical applications. In this paper, a novel tensor-based scheme is proposed...
In addition to helping better understand how the human brain works, the brain-computer interface neuroscience paradigm allows researchers to develop a new class of bioengineering control devices and robots, offering promise for rehabilitation and other medical applications as well as exploring possibilities for advanced human-computer interfaces.
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