Purpose: P300 component of event related potentials in response to visual and auditory stimulation has been widely used in brain–computer interfaces (BCI). In clinical applications, tactile stimulus based on somatosensory electrical stimulation is an alternative for patients with impaired vision or hearing. This study presents an online P300 BCI based on somatosensory electrical stimulation paradigm. P300 signals were elicited by tactile selective attention of electrical stimuli on four fingers. Methods: Fifteen healthy subjects participated in this study. Participants’ task was to focus their attention on the target finger and count the number. The classification of P300 signals was performed by step-wise linear discriminate analysis. Results: The average classification accuracy of the somatosensory BCI was 79.81 ± 7.91%, with the information transfer rate at 4.9 ± 1.3 bits/min. The BCI performance on different time windows was also evaluated in the present study. Conclusions: Our results demonstrate the feasibility of employing somatosensory electrical stimuli to build a practical online P300 BCI without taxing the visual and auditory channel, providing a wider application prospect in clinical applications and daily life. We anticipate our diagram to be a starting point for more explorations on utilizing electrical somatosensory stimuli in conjunction with portable BCI for neural rehabilitation.

Background: In motor imagery brain–computer interface (MI-BCI) research, electroencephalogram (EEG) signals are complex and nonlinear. This complexity and nonlinearity render signal processing and classification challenging when employing traditional linear methods. Information entropy, with its intrinsic nonlinear characteristics, effectively captures the dynamic behavior of EEG signals, thereby addressing the limitations of traditional methods in capturing linear features. However, the multitude of entropy types leads to unclear application scenarios, with a lack of systematic descriptions. Methods: This study conducted a review of 63 high-quality research articles focused on the application of entropy in MI-BCI, published between 2019 and 2023. It summarizes the names, functions, and application scopes of 13 commonly used entropy measures. Results: The findings indicate that sample entropy (16.3%), Shannon entropy (13%), fuzzy entropy (12%), permutation entropy (9.8%), and approximate entropy (7.6%) are the most frequently utilized entropy features in MI-BCI. The majority of studies employ a single entropy feature (79.7%), with dual entropy (9.4%) and triple entropy (4.7%) being the most prevalent combinations in multiple entropy applications. The incorporation of entropy features can significantly enhance pattern classification accuracy (by 8–10%). Most studies (67%) utilize public datasets for classification verification, while a minority design and conduct experiments (28%), and only 5% combine both methods. Conclusions: Future research should delve into the effects of various entropy features on specific problems to clarify their application scenarios. As research methodologies continue to evolve and advance, entropy features are poised to play a significant role in a wide array of fields and contexts.

Brain lateralization is known to enhance cognitive efficiency by reducing redundant processing. Theories such as HAROLD and CRUNCH propose that cognitive decline with age can be compensated by the recruitment of additional bilateral brain regions. However, cognitive compensation is not always effective, and the underlying mechanisms remain unclear, particularly those not related to aging. Low-frequency oscillation (LFO) may be a potential factor in this process. This study investigated the relationship between LFO and cognitive compensation in the prefrontal cortex (PFC) of 28 young adults during a visual verbal working memory task, utilizing functional near-infrared spectroscopy (fNIRS). The participants were categorized into high- and low-performance groups. Changes in oxygenated hemoglobin (Δ[oxy-Hb]), deoxygenated hemoglobin (Δ[deoxy-Hb]), and total hemoglobin (Δ[tot-Hb]) were measured. Both groups exhibited reduced lateralization and increased PFC activation under cognitive load. The results show that only the high-performance group displayed enhanced Δ[oxy-Hb] LFO power, which correlated with behavioral performance. In conclusion, this study found that insufficient LFO is associated with a lack of cognitive resources, which may be due to a deficiency in cerebral autoregulation (CA). This deficiency results in an absence of low-frequency rhythms during cognitive processes, hindering effective coordination between distant brain regions. This provides new insights into the non-aging-related cognitive compensation mechanism.

Using multiple nonlinear techniques, we revealed the existence of chaos in the spontaneous activity of neuronal networks in vitro. The spatial-temporal dynamics of these networks indicated that emergent transition between chaotic behavior and superburst occurred periodically in low-frequency oscillations. An analysis of network-wide activity indicated that chaos was synchronized among different sites. Moreover, we found that the degree of chaos increased as the number of active sites in the network increased during long-term development (over three months in vitro). The chaotic behavior of the dissociated networks had similar spatial-temporal characteristics (rapid transition, periodicity, and synchronization) as the intact brain; however, the degree of chaos depended on the number of active sites at the mesoscopic level. This work could provide insight into neural coding and neurocybernetics. DOI: http://dx.doi.org/10.1103/PhysRevE.81.061903 © 2010 The American Physical Society