Brain-computer interface (BCI) can be summarized as a system that uses online brain information to realize communication between brain and computer. BCI has experienced nearly half a century of development, although it now has a high degree of awareness in the public, but the application of BCI in the actual scene is still very limited. This collection invited some BCI teams in China to report their efforts to promote BCI from laboratory to real scene. This paper summarizes the main contents of the invited papers, and looks forward to the future of BCI.
Bacomics is a unified framework for the interactions of the brain and the outside world, integrating the subject, method, and application mode of brain-apparatus conversation. This article divides the brain-apparatus conversation modes from the perspective of biological and non-biological apparatus, including the brain-biological organ interaction (BAC-1), brain-external non-living equipment and environment interaction (BAC-2), and the fusion agents of these two interactions (BAC-3), and explains the ways and potential applications in different modes.
The aim of the present study was to investigate the alternations of brain functional networks at resting state in the schizophrenia (SCH) patients using voxel-wise degree centrality (DC) method. The resting-state functional magnetic resonance imaging (rfMRI) data were collected from 41 SCH patients and 41 matched healthy control subjects and then analyzed by voxel-wise DC method. The DC maps between the patient group and the control group were compared using by two sample t test. The correlation analysis was also performed between DC values and clinical symptom and illness duration in SCH group. Results showed that compared with the control group, SCH patients exhibited significantly decreased DC value in primary sensorimotor network, and increased DC value in executive control network. In addition, DC value of the regions with obvious differences between the two groups significantly correlated to Positive and Negative Syndrome Scale (PANSS) scores and illness duration of SCH patients. The study showed the abnormal functional integration in primary sensorimotor network and executive control network in SCH patients.
Steady-state visual evoked potential (SSVEP) is one of the commonly used control signals in brain-computer interface (BCI) systems. The SSVEP-based BCI has the advantages of high information transmission rate and short training time, which has become an important branch of BCI research field. In this review paper, the main progress on frequency recognition algorithm for SSVEP in past five years are summarized from three aspects, i.e., unsupervised learning algorithms, supervised learning algorithms and deep learning algorithms. Finally, some frontier topics and potential directions are explored.
In different stages of schizophrenia (SZ), alterations in gray matter volume (GMV) of patients are normally regulated by various pathological mechanisms. Instead of analyzing stage‐specific changes, this study employed a multivariate structural covariance model and sliding‐window approach to investigate the illness duration‐related developmental trajectory of GMV in SZ. The trajectory is defined as a sequence of brain regions activated by illness duration, represented as a sparsely directed matrix. By applying this approach to structural magnetic resonance imaging data from 145 patients with schizophrenia, we observed a continuous developmental trajectory of GMV from cortical to subcortical regions, with an average change occurring every 0.208 years, covering a time window of 20.176 years. The starting points were widely distributed across all networks, except for the ventral attention network. These findings provide insights into the neuropathological mechanism of SZ with a neuroprogressive model and facilitate the development of process for aided diagnosis and intervention with the starting points.