Research papers on brain computer interfaces published today:
- by Mohammad Haghani DogaheBackground and ObjectivesProsthetic hand development is undergoing a transformative phase, blending biomimicry and neural interface technologies to redefine functionality and sensory feedback. This article explores the symbiotic relationship between biomimetic design principles and neural interface technology (NIT) in advancing prosthetic hand capabilities.MethodsDrawing inspiration from biological systems, researchers aim to replicate the intricate movements and capabilities of the human hand…
- by Wei ZhangTransfer learning is the act of using the data or knowledge in a problem to help solve different but related problems. In a brain computer interface (BCI), it is important to deal with individual differences between topics and/or tasks. A kind of capsule decision neural network (CDNN) based on transfer learning is proposed. In order to solve the problem of feature distortion caused by EEG feature extraction algorithm, a deep capsule decision network was constructed. The architecture includes…
- by Peiyao JiaoWireless microsystems for neural signal recording have emerged as a solution to overcome the limitations of tethered systems, which restrict the mobility of subjects and introduce noise interference. However, existing microsystems often face data throughput, signal processing, and long-distance wireless transmission challenges. This study presents a high-performance wireless microsystem capable of 32-channel, 30 kHz real-time recording, featuring Field Programmable Gate Array (FPGA)-based signal…
- by Htoo Wai AungBrain-computer interfaces (BCIs) rely on accurately decoding electroencephalography (EEG) motor imagery (MI) signals for effective device control. Graph neural networks (GNNs) outperform convolutional neural networks (CNNs) in this regard, by leveraging the spatial relationships between EEG electrodes through adjacency matrices. The EEG graph lottery ticket framework, EEG_GLT-Net, featuring the state-of-the-art (SOTA) EEG_GLT adjacency matrix method, has notably enhanced EEG MI signal…
- by Salem MansourCONCLUSION: This study serves as a proof of concept, showcasing the feasibility and acceptability of the proposed Tele BCI-FES system for rehabilitating the upper extremities of stroke survivors. While some participants demonstrated significant improvements in FMA-UE scores, these findings are not generalizable, as they were derived from a small-scale feasibility study. The results should be interpreted cautiously within the study's specific context. Additionally, the intervention was not…
- by Leah HammondINTRODUCTION: Children and young people (CYP) with severe physical disabilities often experience barriers to independent mobility, placing them at risk for developmental impairments and restricting their independence and participation. Pilot work suggests that brain-computer interface (BCIs) could enable powered mobility control for children with motor disabilities. We explored how severely disabled CYP could use BCI to achieve individualized, functional power mobility goals and acquire power…
- by Zhibin JiangINTRODUCTION: Motor imagery electroencephalographic (MI-EEG) signal recognition is used in various brain-computer interface (BCI) systems. In most existing BCI systems, this identification relies on classification algorithms. However, generally, a large amount of subject-specific labeled training data is required to reliably calibrate the classification algorithm for each new subject. To address this challenge, an effective strategy is to integrate transfer learning into the construction of…
- by Chang-Hee HanThe development of conductive polymer-based dry electrodes with high conductivity is promising for practical applications in daily life due to their biocompatibility, flexibility, lightweight, and comfort. The objective of this study is to demonstrate the feasibility of using a novel silicone-based dry electrode for measuring various bioelectrical signals.The silicone-based electrode, manufactured using an optimized polymer matrix, combines high conductivity with flexibility, ensuring superior…
- by Hossein AhmadiExtracting universal, task-independent semantic features from electroencephalography (EEG) signals remains an open challenge. Traditional approaches are often task-specific, limiting their generalization across different EEG paradigms. This study aims to develop a robust, unsupervised framework for learning high-level, task-independent neural representations. Approach: We propose a novel framework integrating convolutional neural networks (CNNs), AutoEncoders, and Transformers to extract…
- by Junhong LuoThe importance of optimizing channel selection for portable brain-computer interface (BCI) technology is increasingly recognized. Effective channel selection reduces computational load and enhances user experience by making BCI systems more comfortable and easier to use. A significant challenge lies in reducing the number of electrodes without compromising decoding accuracy. Although some methods have been proposed in previous studies, these often increase computational load and overlook the…
- by Tan ZhangCONCLUSION: Although pain-induced brain activation was minimal in patients with DoC, enhanced functional connectivity during pain stimulation suggests that the brain continues to process pain information through coordinated activity between regions. The findings highlight the importance of assessing functional connectivity as a potential method for evaluating pain processing in patients with DoC.
- by Yulan XuCONCLUSION: Our findings suggested that repetitive BCI training could improve attention and induce lasting neuroplastic changes in FPNs. It might be a promising rehabilitative strategy for clinical populations with attention deficits. The right PPC may also be an effective target for neuromodulation in diseases with attention deficits.
- by Shuaifei HuangSupernumerary robotic finger (SRF) has shown unique advantages in the field of motor augmentation and rehabilitation, while the development of brain computer interface (BCI) technology has provided the possibility for direct control of SRF. However, the neuroplasticity effects of BCI-actuated SRF (BCI-SRF) training based on the "six finger" motor imagery paradigm are still unclear. This study recruited 20 healthy right-handed participants and randomly assigned them to either a BCI-SRF training…
- by Sining LiBrain-computer interfaces (BCI) acquire electroencephalogram (EEG) signals to effectively address postoperative motor dysfunction in stroke patients by discerning their motor intentions during significant movements. Traditionally, noninvasive BCIs have been constrained by limitations in their usage environments; whereas, invasive BCIs damage neural permanently. Therefore, we proposed a novel interventional BCI, in which electrodes are implanted along the veins into the brain to acquire…
- by Meysam HashemiCurrent clinical methods often overlook individual variability by relying on population-wide trials, while mechanismbased trials remain underutilized in neuroscience due to the brain's complexity. This situation may change through the use of a Virtual Brain Twin (VBT), which is a personalized digital replica of an individual's brain, integrating structural and functional brain data into advanced computational models and inference algorithms. By bridging the gap between molecular mechanisms,…
- by Shuning XueWe share a multi-subject and multi-session (MSS) dataset with 122-channel electroencephalographic (EEG) signals collected from 32 human participants. The data was obtained during serial visual presentation experiments in two paradigms. Dataset of first paradigm consists of around 800,000 trials presenting stimulus sequences at 5 Hz. Dataset of second paradigm comprises around 40,000 trials displaying each image for 1 second. Each participant completed between 1 to 5 sessions on different days,…
- by Tianyu HeSpeech BCIs based on implanted electrodes hold significant promise for enhancing spoken communication through high temporal resolution and invasive neural sensing. Despite the potential, acquiring such data is challenging due to its invasive nature, and publicly available datasets, particularly for tonal languages, are limited. In this study, we introduce VocalMind, a stereotactic electroencephalography (sEEG) dataset focused on Mandarin Chinese, a tonal language. This dataset includes…
- by Beining CaoSteady-State Visual Evoked Potentials (SSVEP) have proven to be practical in Brain-Computer Interfaces (BCI), particularly when integrated with augmented reality (AR) for real-world application. However, unlike conventional computer screen-based SSVEP (CS-SSVEP), which benefits from stable experimental environments, AR-based SSVEP (AR-SSVEP) systems are susceptible to the interference of real-world environment and device instability. Particularly, the performance of AR-SSVEP significantly…
- by Jamila AkhterThe functional near-infrared spectroscopy-based brain-computer interface (fNIRS-BCI) systems recognize patterns in brain signals and generate control commands, thereby enabling individuals with motor disabilities to regain autonomy. In this study hand gripping data is acquired using fNIRS neuroimaging system, preprocessing is performed using nirsLAB and features extraction is performed using deep learning (DL) Algorithms. For feature extraction and classification stack and fft methods are…
- by Deyu ZhaoObjective.With the recent development of visual evoked potential (VEP) based brain-computer interfaces (BCIs), the stimulus paradigm has been continuously innovated, in which the pursuit of higher BCI performance and better user experience has become indispensable.Approach.To optimize the stimulus paradigm, a 12-target online BCI system was designed in this study by adopting flicker for steady-state VEPs, Newton's ring for steady-state motion VEP, and frame rate based video stimulus,…