No abstract

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.

BackgroundMotor imagery (MI) induced EEG patterns are widely used as control signals for brain-computer interfaces (BCIs). Kinetic and kinematic factors have been proved to be able to change EEG patterns during motor execution and motor imagery. However, to our knowledge, there is still no literature reporting an effective online MI-BCI using kinetic factor regulated EEG oscillations. This study proposed a novel MI-BCI paradigm in which users can online output multiple commands by imagining clenching their right hand with different force loads.MethodsEleven subjects participated in this study. During the experiment, they were asked to imagine clenching their right hands with two different force loads (30% maximum voluntary contraction (MVC) and 10% MVC). Multi-Common spatial patterns (Multi-CSPs) and support vector machines (SVMs) were used to build the classifier for recognizing three commands corresponding to high load MI, low load MI and relaxed status respectively. EMG were monitored to avoid voluntary muscle activities during the BCI operation. The event-related spectral perturbation (ERSP) method was used to analyse EEG variation during multiple load MI tasks.ResultsAll subjects were able to drive BCI systems using motor imagery of different force loads in online experiments. We achieved an average online accuracy of 70.9%, with the highest accuracy of 83.3%, which was much higher than the chance level (33%). The event-related desynchronization (ERD) phenomenon during high load tasks was significantly higher than it was during low load tasks both in terms of intensity at electrode positions C3 (p < 0.05) and spatial distribution.ConclusionsThis paper demonstrated the feasibility of the proposed MI-BCI paradigm based on multi-force loads on the same limb through online studies. This paradigm could not only enlarge the command set of MI-BCI, but also provide a promising approach to rehabilitate patients with motor disabilities.

No abstract

Brain computer interfaces (BCIs) based on motor imagery (MI) play an important role in helping to improve and restore the loss of physical function. However, traditional MI-BCIs are limited to the motion intention of gross limb, which places many restrictions on their applications. This study proposes a hybrid paradigm based on MI and the steady-state somatosensory evoked potential, with the aim of improving the spatial resolution of MI recognition. Twelve subjects participated in this study. They performed MI tasks under MI and hybrid conditions. In the MI condition, subjects only performed MI tasks, whereas, in the hybrid condition, they received an electrical stimulus while performing the same tasks. Under the hybrid condition, subjects were required not to pay extra attention to the electrical stimulation. The MI task included imagining clenching the right hand and lifting the right forearm. The classifier was built using the filter bank common spatial pattern algorithm and a support vector machine, and online experiments were used to verify the recognition of two MI tasks. During the online experiments, all subjects were able to output different commands at a recognition accuracy far higher than the random level. The average classification accuracy of the hybrid condition reached 76.39%, with a maximum value of 88.34%, which was about 11% higher than that of the MI condition. Moreover, based on offline data, the classification performance using the event-related desynchronization (ERD) feature under the hybrid condition did not differ significantly from that under the MI condition, indicating that the introduction of electrical stimulation did not interfere in the separability of ERD. The proposed paradigm improved the efficiency of decoding multiple MI locations within a single limb. Despite the introduction of external stimuli, users could still drive the new system in the same way as MI in traditional MI-BCI. INDEX TERMS Motor imagery, event-related desynchronization (ERD), steady-state somatosensory evoked potential (SSSEP), hybrid brain-computer interface.

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.

No abstract

Impaired decision-making has been observed in suicide attempters during the Iowa Gambling Task (IGT). Decision-making performance is influenced by somatic markers and explicit knowledge, but it is still unclear of the influencing role on decision-making performance in suicidal individuals. We aimed to investigate whether there is a decision-making deficit in suicide attempters, suicide ideators, as well as the distinct roles of somatic markers and explicit knowledge wherein. Thirteen suicide attempters, 23 suicide ideators, and 19 healthy controls performed the IGT. Both somatic markers (by the skin conductance responses, SCRs) and explicit knowledge (by the subjective experience rating and a list of questions) were recorded. No significant differences were found among the three groups on IGT performance, explicit knowledge, and anticipatory SCRs. IGT Performance of suicide attempters was positively correlated with explicit knowledge index while behavior performance was positively associated with the SCRs in healthy controls. These results indicate that the suicide attempters seem to apply a compensatory strategy by mostly utilizing explicit knowledge to perform normally as healthy controls in the IGT.