Research papers on brain computer interfaces published today:
- by Xiaoqing ChenMachine learning has been extensively applied to signal decoding in electroencephalogram (EEG)-based brain-computer interfaces (BCIs). While most studies have focused on enhancing the accuracy of EEG-based BCIs, more attention should be given to their security. Recent findings reveal that EEG-based BCIs are vulnerable to adversarial attacks. To address this, we present the first adversarial defense benchmark based on data alignment, aiming to enhance both the accuracy and robustness of EEG-based…
- by Yanci LiuIt is critical for manufacturers to assess customers' aesthetic preferences for various interfaces. However, few studies on neural oscillations for aesthetic judgment have yielded inconsistent results. In this study, we explored the EEG oscillations linked to aesthetic judgments using interface materials (from aesthetic to medium and unaesthetic) along with corresponding scrambled images. Present findings showed that theta-band oscillations to interface were significantly higher for aesthetic…
- by Xueqing DuEpilepsy, a common neurological disorder, is characterized by recurrent seizures that can lead to cognitive, psychological, and neurobiological consequences. The pathogenesis of epilepsy involves neuronal dysfunction at the molecular, cellular, and neural circuit levels. Abnormal molecular signaling pathways or dysfunction of specific cell types can lead to epilepsy by disrupting the normal functioning of neural circuits. The continuous emergence of new technologies and the rapid advancement of…
- by Yansong XuThe anthropomorphic grasping capability of prosthetic hands is critical for enhancing user experience and functional efficiency. Existing prosthetic hands relying on brain-computer interfaces (BCI) and electromyography (EMG) face limitations in achieving natural grasping due to insufficient gesture adaptability and intent recognition. While vision systems enhance object perception, they lack dynamic human-like gesture control during grasping. To address these challenges, we propose a…
- by Dylan ForenzoRecent advancements in signal processing techniques have enabled non-invasive Brain-Computer Interfaces (BCIs) to control assistive devices, like robotic arms, directly with users' EEG signals. However, the applications of these systems are currently limited by the low signal-to-noise ratio and spatial resolution of EEG from which brain intention is decoded. In this study, we propose a motor-imagery (MI) paradigm, inspired by the mechanisms of a computer mouse, that adds an additional "click"…
- by Hyunji KimThis study introduces a Brain-Computer Interface (BCI)-controlled upper limb assistive robot for post-stroke rehabilitation. The system utilizes electroencephalogram (EEG) and electrooculogram (EOG) signals to help users assist upper limb function in everyday tasks while interacting with a robotic hand. We evaluated the effectiveness of this BCI-robot system using the Berlin Bimanual Test for Stroke (BeBiTS), a set of 10 daily living tasks involving both hands. Eight stroke patients participated…
- by Enze TangPeople with schizophrenia (PSZ) showed preserved ability to unconsciously process simple social information (e.g., face and gaze), but not in higher-order cognition (e.g., memory). It is yet unknown how PSZ process social interactions across different cognitive domains. This study systematically investigated the cognitive characteristics of PSZ during social interaction processing from bottom-up perception to top-down memory, and established correlations with schizophrenic symptoms. In two…
- by Wenjing XiongDecoding natural language directly from neural activity is of great interest to people with limited communication means. Being a non-invasive and convenient approach, the electroencephalogram (EEG) offers better portability and wider application potentiality. We present an EEG-to-speech system (ETS) that synthesizes audible, and highly understandable language by EEG of imagined speech. Our ETS applies a specially designed X-shape deep neural network (DNN) to realize an End-to-End correspondence…
- by Fukang ZengAction observation-based brain-computer interface (AO-BCI) can simultaneously elicit steady-state motion visual evoked potential in the occipital region and sensorimotor rhythm in the sensorimotor region, demonstrating substantial potential in neuro-rehabilitation applications. While current AO-BCI research primarily focuses on the younger population, this study conducted a comparative investigation of age-related differences in EEG response to the AO-BCI by enrolling 18 older and 18 younger…
- by Shuo-Yen ChuehBrain Computer Interfaces (BCIs) require substantial cognitive flexibility to optimize control performance. Remarkably, learning this control is rapid, suggesting it might be mediated by neuroplasticity mechanisms operating on very short time scales. Here, we propose a meta plasticity model of BCI learning and skill consolidation at the single cell and population levels comprised of three elements: a) behavioral time scale synaptic plasticity (BTSP), b) intrinsic plasticity (IP) and c) synaptic…
- by Jongha LeeBuilding brain foundation models to capture the underpinning neural dynamics of human behavior requires large functional neural datasets for training, which current implantable Brain-Computer Interfaces (iBCIs) cannot achieve due to the instability of rigid materials in the brain. How can we realize high-density neural recordings with wide brain region access at single-neuron resolution, while maintaining the long-term stability required? In this study, we present a novel approach to overcome…
- by Yiheng XuUnraveling complex cell-type-composition and gene-expression patterns at the cellular spatial resolution is crucial for understanding intricate cell functions in the brain. In this study, we developed Deep Neural Network-based Spatial Cell Typing (DSCT)-an innovative framework for spatial cell typing within spatial transcriptomic data sets. This approach utilizes a synergistic integration of an enhanced gene-selection strategy and a lightweight deep neural network for data training, offering a…
- by Shujhat KhanCONCLUSIONS: Here, we summarise the progress made by BCIs used for communication. Speech decoding directly from the cortex can provide a novel therapeutic method to restore full, embodied communication to patients suffering from tetraplegia who otherwise cannot communicate.
- by Karameldeen OmerCONCLUSIONS: This research highlights the trade-offs between accuracy, mental load, and engagement in BCI-based robot control. The findings support the development of more intuitive assistive robotics, particularly for disabled and elderly users.
- by Jifeng Gong(1) Background: Brain-computer interface (BCI) technology represents a cutting-edge field that integrates brain intelligence with machine intelligence. Unlike BCIs that rely on external stimuli, motor imagery-based BCIs (MI-BCIs) generate usable brain signals based on an individual's imagination of specific motor actions. Due to the highly individualized nature of these signals, identifying individuals who are better suited for MI-BCI applications and improving its efficiency is critical. (2)…
- by Anirban DuttaCONCLUSIONS: Predictive coding and active inference offer a powerful computational framework for understanding and enhancing the SoA in health and disease. The SCAN system serves as a neural hub for integrating motor plans with cognitive and affective processes. Future work should explore the real-time modulation of agency via biofeedback, simulation, and SCAN-targeted non-invasive brain stimulation.
- by Chen FengRecent advances in brain-computer interfaces (BCIs) have demonstrated the potential to decode language from brain activity into sound or text, which has predominantly focused on alphabetic languages, such as English. However, logosyllabic languages, such as Mandarin Chinese, present marked challenges for establishing decoders that cover all characters, due to its unique syllable structures, extended character sets (e.g., over 50,000 characters for Mandarin Chinese), and complex mappings between…
- by Tran Hiep DinhOBJECTIVE: Effective smoothing of electroencephalogram (EEG) signals while maintaining the original signal's features is important in EEG signal analysis and brain-computer interface (BCI). This paper proposes a novel EEG signal-smoothing algorithm and its potential application in cognitive conflict processing.
- by Cogitate ConsortiumDifferent theories explain how subjective experience arises from brain activity^(1,2). These theories have independently accrued evidence, but have not been directly compared³. Here we present an open science adversarial collaboration directly juxtaposing integrated information theory (IIT)^(4,5) and global neuronal workspace theory (GNWT)^(6-10) via a theory-neutral consortium^(11-13). The theory proponents and the consortium developed and preregistered the experimental design, divergent…
- by Haoqi NiThe characteristics of refractory epilepsy change with disease progression. However, relevant studies are scarce due to the difficulty in obtaining long-term multi-site data from patients with epilepsy. This work aimed to provide a long-term brain electrophysiological dataset of 15 pilocarpine-treated rats with temporal lobe epilepsy (TLE). The dataset was constituted by multi-site local field potential (LFP) signal recorded from 12 sites in the Papez circuit in TLE, including spontaneous…