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Objective: Speech brain-computer interfaces (speech BCIs), which convert brain signals into spoken words or sentences, have demonstrated great potential for high-performance BCI communication. Phonemes are the basic pronunciation units. For monosyllabic languages such as Chinese Mandarin, where a word usually contains less than three phonemes, accurate decoding of phonemes plays a vital role. We found that in the neural representation space, phonemes with similar pronunciations are often inseparable, leading to confusion in phoneme classification., Methods: We mapped the neural signals of phoneme pronunciation into a hyperbolic space for a more distinct phoneme representation. Critically, we proposed a hyperbolic hierarchical clustering approach to specifically learn a phoneme-level structure to guide the representation., Results: We found such representation facilitated greater distance between similar phonemes, effectively reducing confusion. In the phoneme decoding task, our approach demonstrated an average accuracy of 75.21% for 21 phonemes and outperformed existing methods across different experimental days., Conclusion: Our approach showed high accuracy in phoneme classification. By learning the phoneme-level neural structure, the representations of neural signals were more discriminative and interpretable., Significance: Our approach can potentially facilitate high-performance speech BCIs for Chinese and other monosyllabic languages.

Objective: Brain-machine interfaces (BMIs) aim to provide direct brain control of devices such as prostheses and computer cursors, which have demonstrated great potential for motor restoration. One major limitation of current BMIs lies in the unstable performance due to the variability of neural signals, especially in online control, which seriously hinders the clinical availability of BMIs., Method: We propose a dynamic ensemble Bayesian filter (DyEnsemble) to deal with the neural variability in online BMI control. Unlike most existing approaches using fixed models, DyEnsemble learns a pool of models that contains diverse abilities in describing the neural functions. In each time slot, it dynamically weights and assembles the models according to the neural signals in a Bayesian framework. In this way, DyEnsemble copes with variability in signals and improves the robustness of online control., Results: Online BMI experiments with a human participant demonstrate that, compared with the velocity Kalman filter, DyEnsemble significantly improves the control accuracy (increases the success rate by 13.9% in the random target pursuit task) and robustness (performs more stably over different experiment days)., Conclusion: Experimental results demonstrate the superiority of DyEnsemble in online BMI control., Significance: DyEnsemble frames a novel and flexible dynamic decoding framework for robust BMIs, beneficial to various neural decoding applications.

We present an implantable brain-computer interface surgical case assisted by robotic navigation system in an elderly patient with tetraplegia caused by cervical spinal cord injury. Left primary motor cortex was selected for implantation of microelectrode arrays based on fMRI location of motor imagery. Robotic navigation system was used during this procedure for precise and stable manipulation. A design of bipartite incision was made to reduce the risk of surgery-related infection and facilitate BCI training. At 1-year follow-up, the neural signals were robust, and this patient was able to control three-dimensional movement of a prosthetic limb without any complications., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.)

Objective: Research on individuals with brain-computer interface (BCI) presents not only technological challenges but ethical challenges (e.g., psychological aspects) as well. We assessed the mental health of a senile patient with tetraplegia after an invasive implantation of BCI and a long-term daily training, in order to provide new experience about the ethical impact of BCI on users and inform future clinical applications of such devices., Methods: This case was a 71-year-old man with tetraplegia for 2 years. Prior to the implant surgery of BCI, and 1 month, 2 months, 3 months, and 9 months after training, a series of tests for cognition, emotion, social support, sleep, and quality of life were performed to evaluate the patient's mental health., Results: Compared with baseline before surgery, the patients' cognition, emotion, social support, sleep, and quality of life improved after the surgery and the long-term daily training. At 3 months post-training, the patient's cognitive score measured by Mini-mental State Examination reached the cutoff point for cognitive impairment in the elderly. Subjective well-being and quality of life showed a slight decline at 9 months post-training compared with that 3 months post-training but remained above the baseline., Conclusion: This study shows the psychological benefits in a senile patient after an invasive BCI implantation and a long-term daily training. BCI ethics is still in its early stages, and further research is needed to understand emerging psychological states of this specific population., (© 2021. Fondazione Società Italiana di Neurologia.)

Objective: Many serious concerns exist in the long-term stability of brain-machine interfaces (BMIs) based on spike signals (single unit activity, SUA; multi unit activity, MUA). Some studies showed local field potentials (LFPs) could offer a stable decoding performance. However, the decoding stability of LFPs was examined only when high quality spike signals were recorded. Here we aim to examine the long-term decoding stability of LFPs over a larger time scale when the quality of spike signals was from good to poor or even no spike was recorded., Approach: Neural signals were collected from motor cortex of three monkeys via silicon arrays over 230, 290 and 690 days post-implantation when they performed 2D center out task. To compare long-term stability between LFPs and spike signals, we examined them in neural signals characteristics, directional tuning properties and offline decoding performance, respectively., Main Results: We observed slow decreasing trends in the number of LFP channels recorded and mean LFP power in different frequency bands when spike signals quality decayed over time. The number of significantly directional tuning LFP channels decreased more slowly than that of tuning SUA and MUA. The variable preferred directions for the same signal features across sessions indicated non-stationarity of neural activity. We also found that LFPs achieved better decoding performance than SUA and MUA in retrained decoder when the quality of spike signals seriously decayed. Especially, when no spike was recorded in one monkey after 671 days post-implantation, LFPs still provided some kinematic information. In addition, LFPs outperformed MUA in long-term decoding stability in a static decoder., Significance: Our results suggested that LFPs were more durable and could provide better decoding performance when spike signals quality seriously decayed. It might be due to their resistance to recording degradation and their high redundancy among channels.

Electrocorticography (ECoG) has been demonstrated as a promising neural signal source for developing brain-machine interfaces (BMIs). However, many concerns about the disadvantages brought by large craniotomy for implanting the ECoG grid limit the clinical translation of ECoG-based BMIs. In this study, we collected clinical ECoG signals from the sensorimotor cortex of three epileptic participants when they performed hand gestures. The ECoG power spectrum in hybrid frequency bands was extracted to build a synchronous real-time BMI system. High decoding accuracy of the three gestures was achieved in both offline analysis (85.7%, 84.5%, and 69.7%) and online tests (80% and 82%, tested on two participants only). We found that the decoding performance was maintained even with a subset of channels selected by a greedy algorithm. More importantly, these selected channels were mostly distributed along the central sulcus and clustered in the area of 3 interelectrode squares. Our findings of the reduced and clustered distribution of ECoG channels further supported the feasibility of clinically implementing the ECoG-based BMI system for the control of hand gestures. [ABSTRACT FROM AUTHOR]

Recent studies showed that the local field potential (LFP) in motor cortex carries information about parameters of limb movements and could be used as a candidate neural signal in brain-machine interfaces to control external devices. However, it is yet to be clear how much information LFP can offer and how it can be effectively used in BMIs. In this article, we systematically evaluated the decoding performance of combining LFP and single-unit activity (SUA) from the primary motor cortex of rats performing the lever-pressing task. The results showed that the decoding power could be significantly improved by combining SUA and LFP in the time-frequency mode, which is based on the separation of LFP into multiple frequency bands. Furthermore, we found that using all frequency bands might be the best choice because it yielded better or no significantly worse results than using low or high frequency bands only. This implies that different frequency components of LFP carry different information. Moreover, we demonstrated that the combination could stabilize the decoding performance even if SUA disappears over time. These results suggest that the different frequency components in the LFP play different roles in kinematics decoding and the combination of LFPs and SUA could be a promising strategy for improving neural decoding in brain-machine interfaces.© 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 165-172, 2011 [ABSTRACT FROM AUTHOR]

Though commonly used, metal electrodes are incompatible with brain tissues, often leading to injury and failure to achieve long-term implantation. Here we report a subdural neural interface of hydrogel functioning as an ionic conductor, and elastomer as a dielectric. We demonstrate that it incurs a far less glial reaction and less cerebrovascular destruction than a metal electrode. Using a cat model, the hydrogel electrode was able to record electrical signals comparably in quality to a metal electrode. The hydrogel-elastomer neural interface also readily facilitated multimodal functions. Both the hydrogel and elastomer are transparent, enabling in vivo optical microscopy. For imaging, cerebral vessels and calcium signals were imaged using two-photon microscopy. The new electrode is compatible with magnetic resonance imaging and does not cause artifact images. Such a new multimodal neural interface could represent immediate opportunity for use in broad areas of application in neuroscience research and clinical neurology. [ABSTRACT FROM AUTHOR]