Inter-stimulus phase coherence in steady-state somatosensory evoked potentials and its application in improving the performance of single-channel MI-BCI

Abstract

Objective. With the development of clinical applications of motor imagery-based brain–computer interfaces (MI-BCIs), a single-channel MI-BCI system that can be easily assembled is an attractive goal. However, due to the low quality of the spectral power features in the traditional MI-BCI paradigm, the recognition performance of current single-channel systems is far lower than that of multi-channel systems, impeding their use in clinical applications. Approach. In this study, the subjects’ right and left hands were stimulated simultaneously at different frequencies to induce steady-state somatosensory evoked potentials (SSSEP). Subjects then performed motor imagery (MI) tasks. A new electroencephalography (EEG) index, inter-stimulus phase coherence (ISPC), was built to measure phase desynchronization of SSSEP caused by MI. Then, ISPC is introduced as a feature into left-hand and right-hand MI recognition. Main results. ISPC analysis found that left-handed MI can cause a significant decrease in phase synchronization in contralateral sensorimotor SSSEP, while right-handed MI has little effect on it, and vice versa. Combining ISPC features with traditional spectral power features, the single-channel left-hand versus right-hand MI recognition accuracy reaches 81.0%, which is much higher than that observed with traditional MI paradigms (about 60%). Significance. This work shows that the hybrid MI-SSSEP paradigm can provide more sensitive EEG features to decode motor intentions, demonstrating its potential for clinical applications.

Improvements in Classification of Left and Right Foot Motor Intention Using Modulated Steady-State Somatosensory Evoked Potential Induced by Electrical Stimulation and Motor Imagery

“…To further study the ISPC feature discrepancy between two unilateral foot MI tasks, the 𝑟 coefficient and corresponding topographical maps at different times were compared, as seen in Figure 5(b). As shown in the lower panel of Figure 5, the ISPC revealed a high discrepancy during the MI task period, which agreed with the conclusion of upper-limb [17], but the results of lower-limb were much smaller. The topographical maps of the 𝑟 coefficient spatial distributions showed that the ISPC discrepancies between leftright foot motor intentions mainly focused on the sensorimotor area near the central area, which was consistent with the previous results shown in Figure 4(c).…”

“…As a modulation signal influenced by MI, the phase synchronization of the SSSEP may also change during the MI task periods. Here, we employed interstimulus phase coherence (ISPC) to measure phase synchronization variations under somatosensory stimulation [17]. Assuming that 𝑓 is the SSSEP frequency, the original EEG 𝑆(𝑡) is first filtered to the [𝑓 − 0.5, 𝑓 + 0.5] SSSEP band, and then the filtered signal 𝑆 (𝑡) is segmented by a sliding window, which moves 1/𝑓 seconds between two adjacent sliding windows.…”

“…To further study the separability of foot motor intention in the SSSEP, we calculated the grand average separability 𝑟 coefficient during the execution of the left/right foot imagery task under the MI-SSSEP paradigm. The calculation process is shown in Formula (4) [17]. The sample signal, 𝑥 _ , of the SSSEP band was composed of several trials in a 28-or 33-Hz frequency band during the left foot imagery task on the 𝑗-th specific channel, where 𝑁 _ is the sample length.…”

“…SSSEP, as a type of steady-state somatosensory characteristic potential, has been introduced into hybrid BCIs in recent years [15]. As it has been proven that the combination of SSSEP features and MI tasks can enhance the spatial resolution of MI recognition and improve the recognition accuracy of upper-limb classification performance [9,16,17], it is natural to assume the good performance of introducing SSSEP into MI-BCI for foot motor intention recognition. The electrical stimulation could produce a stable SSSEP of the same stimulation frequency and activate the cerebral sensory cortex.…”

EEG-based brain–computer interfaces exploiting steady-state somatosensory-evoked potentials: a literature review

“…It is noteworthy that, to a lesser extend, one publication also uses phrase information [42]. The Inter-Stimulus Phase Coherence is computed using the Hilbert transform and the analytic signal form of the EEG.…”