Continuous Wavelet Transform for Decoding Finger Movements From Single-Channel EEG

Human body movements can be reflected in brain signals and collected noninvasively with electroencephalography (EEG). Motor-related signals include sensory motor rhythms (also known as the Mu wave) in the upper-alpha band of 8-13 Hz and slow cortical potentials (SCPs) in the low frequency range of 0.1-5 Hz. This study compares the two signals for decoding finger movements. Human subjects were asked to individually lift each of the five digits of their right hand, at the rate of one every 10 s. EEG was recorded using a bipolar montage between ipsilateral and contralateral motor cortices. Electromyograms were obtained for identifying movement onsets. Linear discriminant analysis (LDA) generated significant performance with SCPs but not with Mu. Meanwhile, continuous wavelet transform (CWT) was applied to SCPs or Mu to create a spectrogram for each finger, showing distinctive amplitude and phase patterns. A dprime-based weighting algorithm was used to extract time-frequency features. With a template-matching paradigm, both SCP and Mu spectrograms yielded significant classification accuracies for multiple subjects, with the highest being >50% (chance = 20%). Interestingly, the index finger was better distinguished with Mu for most of the subjects, whereas the ring finger was better distinguished with SCPs. The CWT algorithm outperformed LDA by better decoding the thumb. This study suggests that the time-frequency characteristics of a single-channel EEG, when phase is preserved, contain critical information on finger movements. SCPs and Mu seem to work in an independent but complementary manner.