1,721,312 research outputs found
2nd workshop of the TOBI project (Translational Issues in BCI Development: User Needs, Ethics, and Technology Transfer).
No full textSource estimation of surface Laplacian transformed EEG potentials on a Talairach-based source space.
No full textAn object oriented approach to biofeedback applications for disabled people
No full textBled, Sloveni
Estimation of Cortical Sources Related to Short-Term Memory in Humans with High-Resolution EEG Recordings and Statistical Probability Mapping
No full textResting state effective connectivity in stroke patients. An EEG study
No full textScientific evidences suggest the possibility of obtaining significant information about the state and the cognitive performances of the brain by using only EEG activity during resting state. In this study graph theory was applied to functional brain networks in order to describe the topographic reorganization of the brain connectivity network related to the resting state condition in a population of 42 stroke patients, with the aim to evaluate deviation from healthy conditions and characterize patients on the basis of their clinical features. Brain connectivity was estimated by means of the spectral estimator Partial Directed Coherence and synthetic graph indices were extracted from the estimated networks. Results showed significant differences between the properties of resting state brain networks of stroke patients and those of healthy subjects. A significant effect of the lesion side on the reorganization after the stroke event was also shown
Neurophysiological constraints of control parameters for a brain computer interface system to support post-stroke motor rehabilitation
Full text linkThe Promotɶr is an all-in-one Brain Computer Interface (BCI)-system developed at Fondazione Santa Lucia (Rome, Italy) to support hand motor imagery practice after stroke. In this paper we focus on the optimization of control parameters for the BCI training. We compared two procedures for the feature selection: in the first, features were selected by means of a manual procedure (requiring “skilled users”), in the second a semiautomatic method, developed by us combining physiological and machine learning approaches, guided the feature selection. EEG-based BCI data set collected from 13 stroke patients were analyzed to the aim. No differences were found between the two procedures (paired-samples t-test, p=0.13). Results suggest that the semiautomatic procedure could be applied to support the manual feature selection, allowing no-skilled users to approach BCI technology for motor rehabilitation of stroke patients
Eye-gaze independent EEG-based brain-computer interfaces for communication
No full textThe present review systematically examines the literature reporting gaze independent interaction modalities in non-invasive brain-computer interfaces (BCIs) for communication. BCIs measure signals related to specific brain activity and translate them into device control signals. This technology can be used to provide users with severe motor disability (e.g. late stage amyotrophic lateral sclerosis (ALS); acquired brain injury) with an assistive device that does not rely on muscular contraction. Most of the studies on BCIs explored mental tasks and paradigms using visual modality. Considering that in ALS patients the oculomotor control can deteriorate and also other potential users could have impaired visual function, tactile and auditory modalities have been investigated over the past years to seek alternative BCI systems which are independent from vision. In addition, various attentional mechanisms, such as covert attention and feature-directed attention, have been investigated to develop gaze independent visual-based BCI paradigms. Three areas of research were considered in the present review: (i) auditory BCIs, (ii) tactile BCIs and (iii) independent visual BCIs. Out of a total of 130 search results, 34 articles were selected on the basis of pre-defined exclusion criteria. Thirteen articles dealt with independent visual BCIs, 15 reported on auditory BCIs and the last six on tactile BCIs, respectively. From the review of the available literature, it can be concluded that a crucial point is represented by the trade-off between BCI systems/paradigms with high accuracy and speed, but highly demanding in terms of attention and memory load, and systems requiring lower cognitive effort but with a limited amount of communicable information. These issues should be considered as priorities to be explored in future studies to meet users' requirements in a real-life scenario. © 2012 IOP Publishing Ltd
Attending to tactile, visual or bimodal targets: effects on the P3 and the relevance for brain machine interfaces
No full textThe P3 is a peak in EEG occurring after the presentation of a target. Research has focused on visual and auditory P3s. However, for controlling brain machine interfaces, tactile P3s are more suitable since tactile stimuli are not noticed by others and keep the user's eyes and ears free. We investigated P3s in response to tactile and visual stimuli unimodally, and bimodally. The tactile stimulus was a burst of vibration delivered by one of eight tactors around the waist. The analogous visual stimulus was a flashed circle in a schematic representation of the tactor layout. Participants attended to the vibrations and/or flashes of the target presented amongst non-targets. Tactile targets evoke stronger P3s than visual targets on frontal and central electrodes; the opposite happens on the occipital channel. Visuo-tactile stimuli only modestly strengthened the P3. We conclude that tactile stimuli applied to the waist are suitable to elicit P3s and have great potential for use in BMIs. In a follow-up study, we demonstrate this by building an example BMI
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