Combining Motor Imagery With Selective Sensation Toward a Hybrid-Modality BCI

IEEE Trans. Neural Syst. Rehabil. Eng.

et al. 2018

Self Cite

Abstract-We proposed a multi-class tactile brain-computer interface that utilizes stimulus-induced oscillatory dynamics. It was hypothesized that somatosensory attention can modulate tactile induced oscillation changes, which can decode different sensation attention tasks. Subjects performed four tactile attention tasks, prompted by cues presented in random order and while both wrists were simultaneously stimulated: 1) selective sensation on left hand (SS-L), 2) selective sensation on right hand (SS-R), 3) bilateral selective sensation (SS-B), and 4) selective sensation suppressed or idle state (SS-S). The classification accuracy between SS-L and SS-R (79.9±8.7%) was comparable with that of a previous tactile BCI system based on selective sensation. Moreover, the accuracy could be improved to an average of 90.3±4.9% by optimal class-pair and frequency-band selection. Three-class discrimination had accuracy of 75.2±8.3%, with the best discrimination reached for the classes SS-L, SS-R and SS-S. Finally, four classes were classified with accuracy of 59.4±7.3%. These results show that the proposed system is a promising new paradigm for multi-class BCI.

Section: T1 (Ss-l Vs Ss-r Vs Ss-b) T2 (Ss-l Vs Ss-r Vs Ss-s) T3 (Ss-lmentioning

confidence: 99%

A Multi-Class Tactile Brain–Computer Interface Based on Stimulus-Induced Oscillatory Dynamics

Yao

Chen

IEEE Trans. Neural Syst. Rehabil. Eng.

et al. 2018

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“…With the advantage of subjective motor intention decoding, this independent BCI modality has attracted extensive interest [8]- [11]. In addition, it has been shown that brain response evoked (transient and steady-state potential) or induced (oscillatory power) by exogenous stimuli can facilitate voluntary attention decoding, resulting in at least three BCI categories: (A) transient BCI [12]- [14], such as the visual P300-based speller; (B) steady-state BCI [15]- [17], such as the steady-state visual evoked potentials (SSVEP); and (C) oscillatory BCI [18], [19], such as the tactile BCI based on stimulus-induced oscillatory dynamics. The diversity of BCI modalities may offer alternative methods when a selected modality fails in achieving an adequate performance.…”

mentioning

confidence: 99%

“…Recently, in a series of studies, we proposed a tactile BCI based on oscillatory dynamics from the somatosensory area of the cortex, which we termed selective sensation (SS) tactile BCI [18], [19], [40], [41]. This approach is based on the observation that mental processing of afferent inflow in the human somatosensory system also induces ERD/ERS [42], [43] [44].…”

mentioning

confidence: 99%

Performance of Brain–Computer Interfacing Based on Tactile Selective Sensation and Motor Imagery

Yao

IEEE Trans. Neural Syst. Rehabil. Eng.

Mrachacz-Kersting

et al. 2018

Self Cite

-A large proportion of users do not achieve adequate control using current non-invasive Brain-computer Interfaces (BCI). This issue has being coined "BCI-Illiteracy", and is observed among BCI modalities. Here, we compare the performance and BCI-illiteracy rate of tactile selective sensation (SS) and motor imagery (MI) BCI, for large subject samples. We analyzed 80 experimental sessions from 57 subjects with two-class SS protocols. For SS, the group average performance was 79.8±10.6%, with 43 out of the 57 subjects (75.4%) exceeding the 70% BCI-illiteracy threshold for left and right hand SS discrimination. When compared to previous results, this tactile BCI outperformed all other tactile BCIs currently available. We also analyzed 63 experiment sessions from 43 subjects with two-class MI BCI protocols, where the group average performance was 77.2±13.3%, with 69.7% of the subjects exceeded the 70% performance threshold for left and right hand MI. For within-subject comparison, the 24 subjects who participated to both the SS and MI experiments, the BCI performance was superior with SS than MI especially in beta frequency band (p<0.05), with enhanced R 2 discriminative information in the somatosensory cortex for the SS modality. Both SS and MI showed a functional dissociation between lower alpha ([8 10] Hz) and upper alpha ([10 13] Hz) bands, with BCI performance significantly better in the upper alpha than the lower alpha (p<0.05) band. In summary, we demonstrated that SS is a promising BCI modality with low BCI illiteracy issue, and has great potential in practical applications reaching large population.

“…This system achieved a mean accuracy of 72% in 11 subjects, when selecting between two targets. By comparison, we have shown that BCI based on tactile selective sensation has largely increased tactile BCI performance by approximately 10%, and this tactile BCI modality based on SS substantially outperformed previous tactile BCI systems, making it potentially applicable to a larger number of users [36], [37]. The ERD/ERS oscillatory response and SSSEP response provide complementary information of the somatosensory input processing.…”

Section: Discussionmentioning

confidence: 92%

Decoding Covert Somatosensory Attention by a BCI System Calibrated With Tactile Sensation

Yao¹,

IEEE Trans. Biomed. Eng.

Mrachacz-Kersting

et al. 2018

Abstract-Objective: We propose a novel calibration strategy to facilitate the decoding of covert somatosensory attention by exploring the oscillatory dynamics induced by tactile sensation. Methods: It was hypothesized that the similarity of the oscillatory pattern between stimulation sensation (SS, real sensation) and somatosensory attentional orientation (SAO) provides a way to decode covert somatic attention. Subjects were instructed to sense the tactile stimulation, which was applied to the left (SS-L) or the right (SS-R) wrist. The BCI system was calibrated with the sensation data and then applied for online SAO decoding. Results: Both SS and SAO showed oscillatory activation concentrated on the contralateral somatosensory hemisphere. Offline analysis showed that the proposed calibration method led to greater accuracy than the traditional calibration method based on SAO only. This is confirmed by online experiments, where the online accuracy on 15 subjects was 78.8±13.1%, with 12 subjects >70% and 4 subject >90%. Conclusion: By integrating the stimulus-induced oscillatory dynamics from sensory cortex, covert somatosensory attention can be reliably decoded by a BCI system calibrated with tactile sensation. Significance: Indeed, real tactile sensation is more consistent during calibration than SAO. This brain-computer interfacing approach may find application for stroke and completely locked-in patients with preserved somatic sensation.