Decoding EEG in Cognitive Tasks With Time-Frequency and Connectivity Masks

Electroencephalogram (EEG) is a measurable window looking into brain dynamics. Brain activities may exhibit different representations while executing different cognitive tasks, which can be recognized by decoding EEG. This is crucial for constructing a brain-computer interface (BCI), which directly bridges between the human brain and external experiments or devices for communication or function restoration. This paper proposed a mask-based approach integrating time-frequency mask (TFM) and connectivity mask (CM) to improve BCI performance. The TFM method does not require the discriminative time-frequency points to be centralized together as the specific-frequency-specific-time (SFST) method does. It can also achieve good performance when discriminative features are scattered. Moreover, this paper also developed a CM method in the spatial domain to extract interchannel connectivity features. The performance of these methods was quantitatively evaluated on three datasets involving different cognitive tasks: 1) a pointing movement dataset; 2) a self-paced finger-tapping dataset in BCI competition II; and 3) a slow cortical potential dataset in BCI competition II. Empirical results of this paper showed that the TFM method outperformed the SFST method on all three datasets and achieved comparable performance to the winning methods in the two BCI competition datasets. The performance was further improved by combining TFM and CM, exceeding that of the winning methods in the BCI competition datasets.