Improving N1 classification by grouping EEG trials with phases of pre-stimulus EEG oscillations

Abstract

A reactive brain-computer interface using electroencephalography (EEG) relies on the classification of evoked ERP responses. As the trial-to-trial variation is evitable in EEG signals, it is a challenge to capture the consistent classification features distribution. Clustering EEG trials with similar features and utilizing a specific classifier adjusted to each cluster can improve EEG classification. In this paper, instead of measuring the similarity of ERP features, the brain states during image stimuli presentation that evoked N1 responses were used to group EEG trials. The correlation between momentary phases of pre-stimulus EEG oscillations and N1 amplitudes was analyzed. The results demonstrated that the phases of time-frequency points about 5.3 Hz and 0.3 s before the stimulus onset have significant effect on the ERP classification accuracy. Our findings revealed that N1 components in ERP fluctuated with momentary phases of EEG. We also further studied the influence of pre-stimulus momentary phases on classification of N1 features. Results showed that linear classifiers demonstrated outstanding classification performance when training and testing trials have close momentary phases. Therefore, this gave us a new direction to improve EEG classification by grouping EEG trials with similar pre-stimulus phases and using each to train unit classifiers respectively.

Keywords: EEG; LDA; N1; Phase; Wavelets.

Fig. 1

The varying period of the Morlet wavelet. The length of the wavelets increase linearly from 1.5 cycles at 4 Hz to 10 cycles at 35 Hz. A is the 1.5-cycle wavelet at 4 Hz and B is the 10-cycle wavelet at 35 Hz. These varying cycles were selected to optimize the trade-off between temporal resolution at lower frequencies and stability at higher frequencies

Fig. 2

a Phase bifurcation index (Φ), averaged across all channels and subjects; b The sum of leave-one-out FDR test results of phase bifurcation index (Φ) of all subjects across all channels

Fig. 3

a The distribution of Φ of preceding stimulus onset each channel averaged across all subjects. b Statistical significance of phase bifurcation index (Φ), averaged across all subjects and the channel POz

Fig. 4

N1 amplitude as a function of the pre-stimulus phase. The amplitudes of N1 fluctuated with the average momentary EEG phases at 5.3 Hz and −300 ms. All of the trials were divided into six bins according to the EEG oscillation phases

Fig. 5

Classified accuracy in different phase bins

Fig. 6

The classification accuracy of three different classifiers. **means p < 0.01

Fig. 7

The cross-validation classified accuracy in different sessions