P300-based brain computer interface: Reliability and performance in healthy and paralysed participants

Piccione, Francesco; Giorgi, Flavio; Tonin, Paolo; Priftis, Konstantinos; Giove, Silvio; Silvoni, Stefano; Palmas, G.; Beverina, Fabrizio

doi:10.1016/j.clinph.2005.07.024

Cited by 284 publications

(195 citation statements)

References 22 publications

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“…On the other hand, voluntary modulation of brain activity-as in the case of MI-allows a more direct and active BCI experience, but the training phase may last longer [6]. Over the last few years both types of system have been widely exploited to demonstrate the possibility of using EEG based BCIs for restoring the control and communication capabilities of disabled people [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

An online EEG BCI based on covert visuospatial attention in absence of exogenous stimulation

et al. 2013

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Objective. In this work we present-for the first time-the online operation of an electroencephalogram (EEG) brain-computer interface (BCI) system based on covert visuospatial attention (CVSA), without relying on any evoked responses. Electrophysiological correlates of pure top-down CVSA have only recently been proposed as a control signal for BCI. Such systems are expected to share the ease of use of stimulus-driven BCIs (e.g. P300, steady state visually evoked potential) with the autonomy afforded by decoding voluntary modulations of ongoing activity (e.g. motor imagery). Approach. Eight healthy subjects participated in the study. EEG signals were acquired with an active 64-channel system. The classification method was based on a time-dependent approach tuned to capture the most discriminant spectral features of the temporal evolution of attentional processes. The system was used by all subjects over two days without retraining, to verify its robustness and reliability. Main results. We report a mean online accuracy across the group of 70.6 ± 1.5%, and 88.8 ± 5.8% for the best subject. Half of the participants produced stable features over the entire duration of the study. Additionally, we explain drops in performance in subjects showing stable features in terms of known electrophysiological correlates of fatigue, suggesting the prospect of online monitoring of mental states in BCI systems. Significance. This work represents the first demonstration of the feasibility of an online EEG BCI based on CVSA. The results achieved suggest the CVSA BCI as a promising alternative to standard BCI modalities.

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Section: Introductionmentioning

confidence: 99%

An online EEG BCI based on covert visuospatial attention in absence of exogenous stimulation

et al. 2013

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“…The authors showed that a target probability of 25 % dependably elicited a P300 response, and that the response remained stable over a period of 10 sessions in people with and without ALS. Piccione et al (2006) also tested a four-choice P300 paradigm with seven healthy volunteers and five individuals with tetraplegia, including one individual with ALS. In this paradigm, the participants attended to one of four flashing arrows to indicate which direction they wished to move a cursor: up, down, left, or right.…”

Section: Introductionmentioning

confidence: 99%

A P300-based brain–computer interface for people with amyotrophic lateral sclerosis

Nijboer

Sellers

Mellinger

et al. 2008

Clinical Neurophysiology

553

421

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Objective-The current study evaluates the efficacy of a P300-based Brain-Computer Interface (BCI) communication device for individuals with advanced ALS.Methods-Participants attended to one cell of a N×N matrix while the N rows and N columns flashed randomly. Each cell of the matrix contained one character. Every flash of an attended character served as a rare event in an oddball sequence and elicited a P300 response. Classification coefficients derived using a stepwise linear discriminant function were applied to the data after each set of flashes. The character receiving the highest discriminant score was presented as feedback.Results-In Phase I, six participants used a 6×6 matrix on 12 separate days with a mean rate of 1.2 selections/min and mean online and offline accuracies of 62% and 82% respectively. In Phase II, four participants used either a 6×6 or a 7×7 matrix to produce novel and spontaneous statements with a mean online rate of 2.1 selections/min and online accuracy of 79%. The amplitude and latency of the P300 remained stable over 40 weeks.Conclusions-Participants could communicate with the P300-based BCI and performance was stable over many months.Significance-BCIs could provide an alternative communication and control technology in the in daily lives of people severely disabled by ALS.

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“…Samples between 550 and 800 ms resulted in (almost) as high classification accuracy as the epoch around the P300 (i.e., between 300 and 550 ms). Several P300 papers show a similar difference between targets and standards [10,18,[47][48][49]. However, though the figures show a negativity after the P300 for targets versus standards, none of the authors comment on the effect.…”

Section: Discussionmentioning

confidence: 64%

Tactile, Visual, and Bimodal P300s: Could Bimodal P300s Boost BCI Performance?

Brouwer¹,

Erp²,

Aloise

et al. 2010

SRX Neuroscience

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The P300 is a positive peak in EEG occurring after presentation of a target stimulus. For brain-computer interfaces (BCIs), eliciting P300s by tactile stimuli would have specific advantages; the display can be hidden under clothes and keeps the user's gaze free. In addition, robust classification is especially important for BCIs. This motivated us to investigate P300s in response to tactile and visual stimuli unimodally and bimodally. Tactile stimuli were delivered by tactors around the participant's waist. Visual stimuli were flashed circles on a monitor, schematically representing the tactors. Participants attended to the vibrations and/or flashes of a "target" presented in a stream of standards. The P300 amplitude for the different modalities was comparable in size and depended on electrode location. Classification accuracy was highest in the bimodal condition. We conclude that bimodal stimuli could enhance classification results within a BCI context compared to unimodal presentations.

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P300-based brain computer interface: Reliability and performance in healthy and paralysed participants

Cited by 284 publications

References 22 publications

An online EEG BCI based on covert visuospatial attention in absence of exogenous stimulation

An online EEG BCI based on covert visuospatial attention in absence of exogenous stimulation

A P300-based brain–computer interface for people with amyotrophic lateral sclerosis

Tactile, Visual, and Bimodal P300s: Could Bimodal P300s Boost BCI Performance?

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