Search results for "brain-computer interface"
showing 10 items of 13 documents
Low-Cost Robotic Guide Based on a Motor Imagery Brain–Computer Interface for Arm Assisted Rehabilitation
2020
Motor imagery has been suggested as an efficient alternative to improve the rehabilitation process of affected limbs. In this study, a low-cost robotic guide is implemented so that linear position can be controlled via the user&rsquo
Neural mechanisms of training an auditory event‐related potential task in a brain–computer interface context
2019
Effective use of brain-computer interfaces (BCIs) typically requires training. Improved understanding of the neural mechanisms underlying BCI training will facilitate optimisation of BCIs. The current study examined the neural mechanisms related to training for electroencephalography (EEG)-based communication with an auditory event-related potential (ERP) BCI. Neural mechanisms of training in 10 healthy volunteers were assessed with functional magnetic resonance imaging (fMRI) during an auditory ERP-based BCI task before (t1) and after (t5) three ERP-BCI training sessions outside the fMRI scanner (t2, t3, and t4). Attended stimuli were contrasted with ignored stimuli in the first-level fMRI…
The eye-tracking computer device for communication in amyotrophic lateral sclerosis
2013
Objective To explore the effectiveness of communication and the variables affecting the eye-tracking computer system (ETCS) utilization in patients with late-stage amyotrophic lateral sclerosis (ALS). Methods We performed a telephone survey on 30 patients with advanced non-demented ALS that were provisioned an ECTS device. Median age at interview was 55 years (IQR = 48–62), with a relatively high education (13 years, IQR = 8–13). A one-off interview was made and answers were later provided with the help of the caregiver. The interview included items about demographic and clinical variables affecting the daily ETCS utilization. Results The median time of ETCS device possession was 15 months …
Mutual information-based feature selection for low-cost BCIs based on motor imagery
2016
In the present study a feature selection algorithm based on mutual information (MI) was applied to electro-encephalographic (EEG) data acquired during three different motor imagery tasks from two dataset: Dataset I from BCI Competition IV including full scalp recordings from four subjects, and new data recorded from three subjects using the popular low-cost Emotiv EPOC EEG headset. The aim was to evaluate optimal channels and band-power (BP) features for motor imagery tasks discrimination, in order to assess the feasibility of a portable low-cost motor imagery based Brain-Computer Interface (BCI) system. The minimal sub set of features most relevant to task description and less redundant to…
UnipaBCI a novel general software framework for brain computer interface
2017
The increasing interest in Brain Computer Interface (BCI) requires new fast, reliable and scalable frameworks that can be used by researchers to develop BCI based high performance applications in efficient and fast ways. In this paper is presented "UnipaBCI", a general software framework for BCI applications based on electroencephalogra-phy (EEG) that can fulfill these new needs. A visual evoked potentials (VEP) application has also been developed using the proposed framework in order to test the modular architecture and the overall performance. Different types of users (beginners and experts in BCI) have been involved during the "UnipaBCI" experimental test and they have exhibited good and…
The human-computer connection: An overview of brain-computer interfaces
2018
This article introduces the field of brain-computer interfaces (BCI), which allows the control of devices without the generation of any active motor output but directly from the decoding of the user’s brain signals. Here we review the current state of the art in the BCI field, discussing the main components of such an interface and illustrating ongoing research questions and prototypes for controlling a large variety of devices, from virtual keyboards for communication to robotics systems to replace lost motor functions and even clinical interventions for motor rehabilitation after a stroke. The article concludes with some insights into the future of BCI.
Preserved somatosensory discrimination predicts consciousness recovery in unresponsive wakefulness syndrome
2017
Objective: To assess somatosensory discrimination and command following using a vibrotactile P300-based Brain-Computer Interface (BCI) in Unresponsive Wakefulness Syndrome (UWS), and investigate the predictive role of this cognitive process on the clinical outcomes.Methods: Thirteen UWS patients and six healthy controls each participated in two experimental runs in which they were instructed to count vibrotactile stimuli delivered to the left or right wrist. A BCI determined each subject's task performance based on EEG measures. All of the patients were followed up six months after the BCI assessment, and correlations analysis between accuracy rates and clinical outcome were investigated.Re…
A Human-Humanoid Interaction Through the Use of BCI for Locked-In ALS Patients Using Neuro-Biological Feedback Fusion.
2018
This paper illustrates a new architecture for a human–humanoid interaction based on EEG-brain computer interface (EEG-BCI) for patients affected by locked-in syndrome caused by Amyotrophic Lateral Sclerosis (ALS). The proposed architecture is able to recognise users’ mental state accordingly to the biofeedback factor $\text {B}_{\text f}$ , based on users’ attention, intention, and focus, that is used to elicit a robot to perform customised behaviours. Experiments have been conducted with a population of eight subjects: four ALS patients in a near locked-in status with normal ocular movement and four healthy control subjects enrolled for age, education, and computer expertise. The results s…
Composing only by thought: Novel application of the P300 brain-computer interface.
2017
The P300 event-related potential is a well-known pattern in the electroencephalogram (EEG). This kind of brain signal is used for many different brain-computer interface (BCI) applications, e.g., spellers, environmental controllers, web browsers, or for painting. In recent times, BCI systems are mature enough to leave the laboratories to be used by the end-users, namely severely disabled people. Therefore, new challenges arise and the systems should be implemented and evaluated according to user-centered design (USD) guidelines. We developed and implemented a new system that utilizes the P300 pattern to compose music. Our Brain Composing system consists of three parts: the EEG acquisition d…
Hippocampal ripple-contingent training accelerates trace eyeblink conditioning and retards extinction in rabbits.
2010
There are at least two distinct oscillatory states of the hippocampus that are related to distinct behavioral patterns. Theta (4–12 Hz) oscillation has been suggested to indicate selective attention during which the animal concentrates on some features of the environment while suppressing reactivity to others. In contrast, sharp-wave ripples (∼200 Hz) can be seen in a state in which the hippocampus is at its most responsive to any kind of afferent stimulation. In addition, external stimulation tends to evoke and reset theta oscillation, the phase of which has been shown to modulate synaptic plasticity in the hippocampus. Theoretically, training on a hippocampus-dependent learning task conti…