Search results for "Brain–computer interface"
showing 10 items of 35 documents
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 Possibilities of Using BCI Technology in Biomedical Engineering
2018
The paper presents capabilities of building devices dedicated for persons with heavy mobility dysfunction and indicates the role of interfaces connecting brain with computer (Brain Computer Interface, BCI). Impulses coming from closing eyes, clenching teeth, and tongue movement were proposed as optimal in controlling the applications that manage executable systems. A group of electrodes giving a strong electric signal characteristic for the activity were designated and on the basis of conducted research a proposition of a scientific project concerning building of supporting devices for persons with heavy mobility dysfunction was presented.
Dorsal Column Nuclei Neural Signal Features Permit Robust Machine-Learning of Natural Tactile- and Proprioception-Dominated Stimuli
2020
Neural prostheses enable users to effect movement through a variety of actuators by translating brain signals into movement control signals. However, to achieve more natural limb movements from these devices, the restoration of somatosensory feedback is required. We used feature-learnability, a machine-learning approach, to assess signal features for their capacity to enhance decoding performance of neural signals evoked by natural tactile and proprioceptive somatosensory stimuli, recorded from the surface of the dorsal column nuclei (DCN) in urethane-anesthetized rats. The highest performing individual feature, spike amplitude, classified somatosensory DCN signals with 70% accuracy. The hi…
An Intracortical Implantable Brain-Computer Interface for Telemetric Real-Time Recording and Manipulation of Neuronal Circuits for Closed-Loop Interv…
2021
Recording and manipulating neuronal ensemble activity is a key requirement in advanced neuromodulatory and behavior studies. Devices capable of both recording and manipulating neuronal activity brain-computer interfaces (BCIs) should ideally operate un-tethered and allow chronic longitudinal manipulations in the freely moving animal. In this study, we designed a new intracortical BCI feasible of telemetric recording and stimulating local gray and white matter of visual neural circuit after irradiation exposure. To increase the translational reliance, we put forward a Göttingen minipig model. The animal was stereotactically irradiated at the level of the visual cortex upon defining the targe…
On Human–Computer Interaction in Brain–Computer Interfaces
2014
In this chapter, theoretical reflections on human–computer interaction in brain–computer interfaces (BCIs) are combined with the results of an empirical investigation concerning non-invasive EEG-based BCI users’ experiences with this technology. After a short overview of transhumanist visions in the field of neurotechnology this text discusses some anthropological positions concerning interaction between man and technical devices. The focus will be on the concept of “transparency”. Then some empirical results of a pilot study which investigated BCI users’ experiences concerning human–computer interaction in BCI use are presented and discussed against the anthropological background.
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.
The Users’ Perspective
2014
In this chapter 20 former research subjects in brain–computer interface (BCI) studies answer questions concerning their experiences with this novel technology. They talk about their state of information, their expectations, their achievements, and also their disappointments. The answers display a wide spectrum of attitudes and personal assessments ranging from enthusiastic appreciation to skeptical reservation. Many users mention the current shortcomings but also stress the potential of the tested technology.
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…