BCI

Brain-computer interfaces (BCI) using electroencephalography provide a noninvasive method for users to interact with external devices without the need for muscle activation. While noninvasive BCIs have the potential to improve the quality of lives of healthy and motor-impaired individuals, they currently have limited applications due to inconsistent performance and low degrees of freedom. In this study, we use deep learning (DL)-based decoders for online continuous pursuit (CP), a complex BCI task requiring the user to track an object in 2D space...

The electroencephalogram (EEG) signal is the key signal carrier of the brain-computer interface (BCI) system. The EEG data collected by the whole-brain electrode arrangement is conducive to obtaining higher information representation. Personalized electrode layout, while ensuring the accuracy of EEG signal decoding, can also shorten the calibration time of BCI and has become an important research direction. This paper reviews the EEG signal channel selection methods in recent years, conducts a comparative analysis of the combined effects of different channel selection methods and different classification algorithms, obtains the commonly used channel combinations in motor imagery, P300 and other paradigms in BCI, and explains the application scenarios of the channel selection method in different paradigms are discussed, in order to provide stronger support for a more accurate and portable BCI system...

The brain-computer interface (BCI) allows the human or animal brain to directly interact with the external environment through the neural interfaces, thus playing the role of monitoring, protecting, improving/restoring, enhancing, and replacing. Recording electrophysiological information such as brain neural signals is of great importance in health monitoring and disease diagnosis. According to the electrode position, it can be divided into non-implantable, semi-implantable, and implantable. Among them, implantable neural electrodes can obtain the highest-quality electrophysiological information, so they have the most promising application...

BACKGROUND: To enhance the information transfer rate (ITR) of a steady-state visual evoked potential (SSVEP)-based speller, more characters with flickering symbols should be used. Increasing the number of symbols might reduce the classification accuracy. A hybrid brain-computer interface (BCI) improves the overall performance of a BCI system by taking advantage of two or more control signals. In a simultaneous hybrid BCI, various modalities work with each other simultaneously, which enhances the ITR...

Human cooperation relies on key features of social interaction in order to reach desirable outcomes. Similarly, human-robot interaction may benefit from integration with human-human interaction factors. In this paper, we aim to investigate brain-to-brain coupling during motor imagery (MI)-based brain-computer interface (BCI) training using eye-contact and hand-touch interaction. Twelve pairs of friends (experimental group) and 10 pairs of strangers (control group) were recruited for MI-based BCI tests concurrent with electroencephalography (EEG) hyperscanning...

Recent advancements in neural probe technology have become pivotal in both neuroscience research and the clinical management of neurological disorders. State-of-the-art developments have led to the advent of multichannel, high-density bidirectional neural interfaces that are adept at both recording and modulating neuronal activity within the central nervous system. Despite this progress, extant bidirectional probes designed for simultaneous recording and stimulation are beset with limitations, including elicitation of inflammatory responses and insufficient charge injection capacity...

Motor imagery electroencephalography (EEG) signals have garnered attention in brain-computer interface (BCI) research due to their potential in promoting motor rehabilitation and control. However, the limited availability of labeled data poses challenges for training robust classifiers. In this study, we propose a novel data augmentation method utilizing an improved Deep Convolutional Generative Adversarial Network with Gradient Penalty (DCGAN-GP) to address this issue. We transformed raw EEG signals into two-dimensional time-frequency maps and employed a DCGAN-GP network to generate synthetic time-frequency representations resembling real data...

EEG signals combined with deep learning play an important role in the study of human-computer interaction. However, the limited dataset makes it challenging to study EEG signals using deep learning methods. Inspired by the GAN network in image generation, this paper presents an improved generative adversarial network model L-C-WGAN-GP to generate artificial EEG data to augment training sets and improve the application of BCI in various fields. The generator consists of a long short-term memory (LSTM) network and the discriminator consists of a convolutional neural network (CNN) which uses the gradient penalty-based Wasserstein distance as the loss function in model training...

The rapid serial visual presentation-based brain-computer interface (RSVP-BCI) system achieves the recognition of target images by extracting event-related potential (ERP) features from electroencephalogram (EEG) signals and then building target classification models. Currently, how to reduce the training and calibration time for classification models across different subjects is a crucial issue in the practical application of RSVP. To address this issue, a zero-calibration (ZC) method termed Attention-ProNet, which involves meta-learning with a prototype network integrating multiple attention mechanisms, was proposed in this study...

Brain-computer interfaces (BCIs) that reconstruct and synthesize speech using brain activity recorded with intracranial electrodes may pave the way toward novel communication interfaces for people who have lost their ability to speak, or who are at high risk of losing this ability, due to neurological disorders. Here, we report online synthesis of intelligible words using a chronically implanted brain-computer interface (BCI) in a man with impaired articulation due to ALS, participating in a clinical trial (ClinicalTrials...

Bovine respiratory disease (BRD) presents a challenge to farmers all over the globe not only because it can have significant impacts on welfare and productivity, but also because diagnosis can prove challenging. Several clinical scoring systems have been developed to aid farmers in making consistent early diagnosis, 2 examples being the Wisconsin (WCS) and the California (CALIF) systems. Neither of these systems were developed in or for use in a temperate environment. As environment may lead to changes in BRD presentation, the weightings and cut offs designed for one environmental presentation of BRD may not be appropriate when used in a temperate climate...

Brain-computer interfaces (BCIs) aim at the non-invasive investigation of brain activity for supporting communication and interaction of the users with their environment by means of brain-machine assisted technologies. Despite technological progress and promising research aimed at understanding the influence of human factors on BCI effectiveness, some topics still remain unexplored. The aim of this article is to discuss why it is important to consider the language of the user, its embodied grounding in perception, action and emotions, and its interaction with cultural differences in information processing in future BCI research...

Steady-state visual evoked potential brain-computer interfaces (SSVEP-BCI) have attracted significant attention due to their ease of deployment and high performance in terms of information transfer rate (ITR) and accuracy, making them a promising candidate for integration with consumer electronics devices. However, as SSVEP characteristics are directly associated with visual stimulus attributes, the influence of stereoscopic vision on SSVEP as a critical visual attribute has yet to be fully explored. Meanwhile, the promising combination of virtual reality (VR) devices and BCI applications is hampered by the significant disparity between VR environments and traditional 2D displays...

BACKGROUND: Brain-computer interface (BCI) technology offers children with quadriplegic cerebral palsy unique opportunities for communication, environmental exploration, learning, and game play. Research in adults demonstrates a negative impact of fatigue on BCI enjoyment, while effects on BCI performance are variable. To date, there have been no pediatric studies of BCI fatigue. The purpose of this study was to assess the effects of two different BCI paradigms, motor imagery and visual P300, on the development of self-reported fatigue and an electroencephalography (EEG) biomarker of fatigue in typically developing children...

Transfer learning has become an important issue in the brain-computer interface (BCI) field, and studies on subject-to-subject transfer within the same dataset have been performed. However, few studies have been performed on dataset-to-dataset transfer, including paradigm-to-paradigm transfer. In this study, we propose a signal alignment for P300 event-related potential (ERP) signals that is intuitive, simple, computationally less expensive, and can be used for cross-dataset transfer learning.

Approach...

Sound production during feeding by the pot-bellied seahorse, Hippocampus abdominalis, was quantified with an observation of clicks (acoustic signal) and snicks (visual behavior). Female, male, and juvenile seahorses had feeding sounds characterized for peak (dominant) frequency (Hz), sound pressure level (SPL), and duration (ms). Subject body size and condition was estimated by standard length (SL, cm), to determine an estimate of body condition index (BCI). An inverse correlation between mean peak frequency (Hz) of clicks and SL was found for females...

Magnetoencephalography (MEG) is a non-invasive technique that can precisely capture the dynamic spatiotemporal patterns of the brain by measuring the magnetic fields arising from neuronal activity along the order of milliseconds. Observations of brain dynamics have been used in cognitive neuroscience, the diagnosis of neurological diseases, and the brain-computer interface (BCI). In this study, we outline the basic principle, signal processing, and source localization of MEG, and describe its clinical applications for cognitive assessment, the diagnoses of neurological diseases and mental disorders, preoperative evaluation, and the BCI...

OBJECTIVE: To determine the benefits of binaural hearing rehabilitation in patients with monaural conductive or mixed hearing loss treated with a unilateral bone conduction implant (BCI). METHODS: This monocentric study includes 7 patients with monaural conductive or mixed hearing loss who underwent surgical implantation of a unilateral BCI (Bonebridge, Med-El). An ITA Matrix test was performed by each patient included in the study - without and with the BCI and in three different settings - to determine the summation effect, squelch effect and head shadow effect...


Brain-Computer Interfaces (BCIs) have the potential to reinstate lost communication faculties. Results from speech decoding studies indicate that a usable speech BCI based on activity in the sensorimotor cortex (SMC) can be achieved using subdurally implanted electrodes. However, the optimal characteristics for a successful speech implant are largely unknown. We address this topic in a high field blood oxygenation level dependent (BOLD) functional Magnetic Resonance Imaging (fMRI) study, by assessing the decodability of spoken words as a function of hemisphere, gyrus, sulcal depth, and position along the ventral/dorsal-axis...


Invasive brain-computer interfaces (BCIs) are promising communication devices for severely paralyzed patients. Recent advances in intracranial electroencephalography (iEEG) coupled with natural language processing have enhanced communication speed and accuracy. It should be noted that such a speech BCI uses signals from the motor cortex. However, BCIs based on motor cortical activities may experience signal deterioration in users with motor cortical degenerative diseases such as amyotrophic lateral sclerosis (ALS)...