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Objective. We proposed a brain–computer interface (BCI) based visual-haptic neurofeedback training (NFT) by incorporating synchronous visual scene and proprioceptive electrical stimulation feedback. The goal of this work was to improve sensorimotor cortical activations and classification performance during motor imagery (MI). In addition, their correlations and brain network patterns were also investigated respectively. Approach. 64-channel electroencephalographic (EEG) data were recorded in nineteen healthy subjects during MI before and after NFT. During NFT sessions, the synchronous visual-haptic feedbacks were driven by real-time lateralized relative event-related desynchronization (lrERD). Main results. By comparison between previous and posterior control sessions, the cortical activations measured by multi-band (i.e. alpha_1: 8–10 Hz, alpha_2: 11–13 Hz, beta_1: 15–20 Hz and beta_2: 22–28 Hz) absolute ERD powers and lrERD patterns were significantly enhanced after the NFT. The classification performance was also significantly improved, achieving a ~9% improvement and reaching ~85% in mean classification accuracy from a relatively poor performance. Additionally, there were significant correlations between lrERD patterns and classification accuracies. The partial directed coherence based functional connectivity (FC) networks covering the sensorimotor area also showed an increase after the NFT. Significance. These findings validate the feasibility of our proposed NFT to improve sensorimotor cortical activations and BCI performance during motor imagery. And it is promising to optimize conventional NFT manner and evaluate the effectiveness of motor training.

Conventional noninvasive electroencephalogram (EEG) is limited to poor spatial resolution due to volume conduction effect. To overcome this limitation, the acoustoelectric effect (AE) based acoustoelectric brain imaging (ABI) is proposed for mapping brain electrical activity in a high temporal and spatial resolution. Through phantom and vivo rat brain experiments, this study investigated a biological current source coding mechanism with pulse focused ultrasound (PFU) at pulse repetition frequency (PRF). First, the current source coding mechanism is deduced in theory. Then, with phantom experiment, the coding relationship between AE signal and PRF is investigated in details. With different PRFs, including 100 Hz, 200 Hz, 500 Hz and 1 kHz, amplitude spectrum analysis results indicate that obvious high amplitude response of AE signal appear at each PRF and corresponding harmonic frequencies. And for different current sources of 10 Hz and 30 Hz, the AE signal oscillate at the the same frequency as corresponding PRF. Additionally, for each PRF, the decoded AE signal is of the same frequency and phase with the current source. Finally, coding mechanism is further validated in vivo rat experiment with different PRFs, including 500 Hz, 1 kHz and 2 kHz. The AE signal envelope and decoded AE signal both have significant correlation with low frequency EEG with ultrasound not only in the low frequency band but also in specific frequency. Also, the mean amplitude of delta rhythm respectively calculated from envelope of AE signal and decoded AE signal are obviously higher than the other rhythms which reflects the brain state of anesthesia or lethargy. These theory and experiment results validate that PFU has a coding effect on current source at PRF and demonstrate the feasibility of restoring current source from the coded AE signal which are critical for making ABI a clinical neuroimaging technique. INDEX TERMS Acoustoelectric effect, biomedical current source, coding mechanism, focused ultrasound, pulse repetition frequency.