Latest Publications

Over the past decade, techniques enabling bidirectional modulation of neuronal activity with single-cell precision have rapidly advanced in the form of two-photon optogenetic stimulation. Unlike conventional electrophysiological approaches or one-photon optogenetics, which inevitably activate many neurons surrounding the target, two-photon optogenetics can drive hundreds of specifically targeted neurons simultaneously, with stimulation patterns that can be flexibly and rapidly reconfigured. In…

INTRODUCTION: This study proposes advanced neural network architectures for classifying specific motor-related electroencephalography (EEG) tasks, employing deep feature extraction techniques. We analyzed EEG data from the MILimbEEG dataset, consisting of recordings from 60 individuals as they performed eight distinct motor movements: baseline with eyes open, left-hand closing, right-hand closing, dorsiflexion and plantarflexion of both the left and right feet, as well as rest periods between…

Photoacoustic (PA) stimulation is an emerging technology aiming to modulate neuronal activity safely, precisely, and efficiently without genetic modification. Owing to their strong light absorption, easy to process in the composite, nanomaterials have been utilized as PA interfaces developed for high spatial and temporal resolution PA stimulation without thermal damage in vitro and in vivo. This topical review introduces the theory of PA generation and summarizes the nanomaterials used in PA…

INTRODUCTION: Electroencephalogram (EEG) emotion recognition is attracting increasing attention in the field of brain-computer interface due to its strong objectivity and non-forgery. However, cross-subject emotion recognition is complicated by individual variability, limited availability of EEG data, and interference in certain channels during EEG acquisition.

INTRODUCTION: Ultra-High-Density Electroencephalography (uHD EEG) has gained increasing attention for its potential in individual finger decoding. However, accurately classifying these movements remains challenging due to the subtle spatial overlaps in cortical activity, which standard architectures often fail to isolate.

Evoked potentials (EPs) are increasingly explored as objective neurophysiological biomarkers to complement scale-based assessment in stroke rehabilitation. This narrative review summarizes current evidence on the use of somatosensory evoked potentials (SEPs), motor evoked potentials (MEPs), and event-related potentials (ERPs) for monitoring recovery and guiding therapy. We first outline the physiological basis and stroke-relevant features of each modality, then synthesize data on how EP measures…

Reconstructing speech from neural recordings is crucial for understanding human speech coding and developing brain-computer interfaces (BCIs). However, existing methods trade off acoustic richness (pitch, prosody) for linguistic intelligibility (words, phonemes). To overcome this limitation, we propose a dual-path framework to concurrently decode acoustic and linguistic representations. The acoustic pathway uses a long-short term memory (LSTM) decoder and a high-fidelity generative adversarial…

CONCLUSION: Our findings suggest that video game-based digital therapeutics with EEG-informed real-time neurofeedback can effectively enhance attention in children with ADHD. The results support the potential of using adaptive neurofeedback with portable devices to enhance intervention effects.

Objective Motor learning is key to successful neuro-rehabilitation. Combinations of Brain-Computer Interfaces (BCIs) and repetitive transcranial magnetic stimulation (rTMS) have been proposed for neurorehabilitation following conditions such as stroke. However, rTMS is typically delivered via a fixed protocol without taking into consideration the current brain states of participants. We propose a new BCI-based rTMS delivery protocol for supporting motor learning. Specifically,…

OBJECTIVE: Loss of oligodendrocytes (OLs) and myelin impairs neuronal firing and network stability, whereas enhancing oligodendrogenesis with clemastine improves electrophysiological stability in cortex and, to a lesser extent, hippocampus. Conditional depletion of Fus in OLs (FusOLcKO) drives developmentally regulated increases in myelin thickness via enhanced cholesterol biosynthesis. Here, we investigated whether Fus-depleted OLs differentially affect long-term extracellular recordings across…

Steady state visually evoked potentials (SSVEPs) are a popular type of control signals in brain-computer interfaces (BCIs), in which they are typically elicited by observing a visual stimulus flashing at a specific frequency. For some patients, using SSVEP as control signal for a BCI can be difficult, for instance if they are unable to focus their gaze over the visual stimuli. To address this issue, some approaches were presented to design a gaze-independent SSVEP-controlled BCI but some…

Semantic decoding is a crucial approach for investigating the neural mechanisms underlying language processing and representation. Informed by brain-computer interface (BCI) technology, this study investigated methods for decoding semantic information, with an emphasis on the neural representations of semantics in language perception. Due to the limited availability of electroencephalography (EEG) datasets containing Chinese linguistic stimuli, we have specifically designed a semantic task…

Electroencephalography (EEG)-based brain computer interface (BCI) systems hold significant promise across diverse applications; however, their performance is compromised by pervasive physiological artifacts that degrade signal fidelity. While current deep neural networks (DNNs) improve artifact rejection, their high computational cost precludes deployment in wearable BCIs systems. Here, we introduce STAND-Net (Spiking Temporal Attention autoeNcoDer Network), a neuromorphic architecture that…

Implantable brain-computer interfaces (iBCIs) aim to restore communication in individuals with severe motor impairments. For good iBCI performance, it is important to target an optimal location. In this study, we used high-resolution 7-Tesla functional magnetic resonance imaging (fMRI) to map the spatial distribution of brain activity that can discriminate between a large number of hand gestures. Ten able-bodied participants performed 20 different unimanual hand gestures. Using support vector…

CONCLUSIONS: The study confirmed the correlation between PKD and the loss-of-function of Kir4.1 resulted from heterozygous KCNJ10 variants. The distribution bias of PKD-related KCNJ10 variants as well as the male predominance in affected individuals shed light on the mechanism investigation of this subtype of PKD. © 2026 International Parkinson and Movement Disorder Society.

Accurate and temporary monitoring of brain activity is essential for diagnosing and treating neurological diseases. Conventional nondegradable electrocorticogram (ECoG) devices require removal surgery, thus increasing the risk of infection and tissue damage. Moreover, existing devices typically fail to conform to soft, dynamic brain tissue, thus resulting in unsatisfactory adhesion, signal loss, and mechanical mismatch. Herein, we present the development of a brain-adhesive sensor (B-Sensor)…

Motor imagery EEG (MI-EEG) decoding remains challenging due to low signal-to-noise ratios and pronounced inter-subject variability. Although end-to-end deep models reduce reliance on manual feature engineering, many existing architectures may introduce temporal leakage through non-causal operations and often rely on fixed spatial topologies that cannot accommodate subject- and trial-specific connectivity patterns. Approach. We propose MAGCANet, which integrates five core components: (i) a…

OBJECTIVE: Speech brain-computer interfaces (BCIs) can restore speech features like articulatory movements from brain activity. However, for individuals with vocal tract paralysis, lack of articulatory movements can pose a challenge for speech BCI development. To address this challenge, our study aims at extracting generalizable articulatory features from a group of native Dutch speakers and reconstructing these features from brain data of a separate group of able-bodied individuals.

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PURPOSE: Brain-computer interfaces (BCIs) offer a pathway to restore ambulation in indi-viduals with spinal cord injury (SCI). However, existing BCI systems for gait are unidirectional and lack sensory feedback. This study aimed to demonstrate that a bidirectional brain-computer interface (BDBCI) can simultaneously enable real-time brain-controlled walking and artificial leg sensation via electrical stimulation of the sensory cortex.