A Novel Brain-Computer Interface Approach Designed for Dynamic Target Selection

2016 ◽  
Author(s):  
Yaru Liu ◽  
Yadong Liu ◽  
Yang Yu ◽  
Jun Jiang ◽  
Zongtan Zhou ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Target Selection ◽  
Computer Interface ◽  
Dynamic Target

scholarly journals Difference in Somatosensory Event-Related Potentials in the Blind Subjects Leads to Better Performance in Tactile P300 BCI

2021 ◽  
Author(s):  
Rafael Grigoryan ◽  
Dariya Goranskaya ◽  
Andrey Demchinsky ◽  
Ksenia Ryabova ◽  
Denis Kuleshov ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Target Selection ◽  
Event Related Potentials ◽  
Event Related Potential ◽  
Computer Interface ◽  
Specific Task ◽  
Related Potentials ◽  
The Difference ◽  
Braille Display ◽  
Cognitive Event

Abstract In this study, we created 8-command P300 tactile brain-computer interface, running on minimally modified consumer Braille display, and tested it on 10 blind subjects and 10 sighted controls with two stimuli types, differing in size. Larger stimuli provide better BCI performance both in blind and sighted participants than smaller stimuli. With large stimuli, median target selection accuracy in the blind group was 95%, which is 27% more than sighted controls (p < 0.05), suggesting that blind subjects are not only able to use tactile brain-computer interface but also can achieve superior results in comparison with sighted subjects. The difference in event-related potentials between groups is located in frontocentral sites around 300 ms post-stimulus and corresponds with early cognitive event-related potential components. Blind subjects have higher amplitude and shorter latency of ERPs. This effect was consistent across stimuli types. This is the first study to evaluate differences in event-related potentials between blind and sighted subjects in a BCI-specific task.

scholarly journals BCI Could Make Old Two-Player Games Even More Fun: A Proof of Concept with “Connect Four”

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Emmanuel Maby ◽  
Margaux Perrin ◽  
Olivier Bertrand ◽  
Gaëtan Sanchez ◽  
Jérémie Mattout
Keyword(s):  
Brain Activity ◽  
Brain Computer Interface ◽  
Target Selection ◽  
Computer Interface ◽  
Proof Of Concept ◽  
Disabled People ◽  
Future Studies ◽  
P300 Speller ◽  
Average Accuracy ◽  
Player Game

We present a brain-computer interface (BCI) version of the famous “Connect Four”. Target selection is based on brain event-related responses measured with nine EEG sensors. Two players compete against each other using their brain activity only. Importantly, we turned the general difficulty of producing a reliable BCI command into an advantage, by extending the game play and rules, in a way that adds fun to the game and might well prove to trigger up motivation in future studies. The principle of this new BCI is directly inspired from our own implementation of the classical P300 Speller (Maby et al. 2010, Perrin et al. 2011). We here establish a proof of principle that the same electrophysiological markers can be used to design an efficient two-player game. Experimental evaluation on two competing healthy subjects yielded an average accuracy of 82%, which is in line with our previous results on many participants and demonstrates that the BCI “Connect Four” can effectively be controlled. Interestingly, the duration of the game is not significantly affected by the usual slowness of BCI commands. This suggests that this kind of BCI games could be of interest to healthy players as well as to disabled people who cannot play with classical games.

scholarly journals A 36-Class Bimodal ERP Brain-Computer Interface Using Location-Congruent Auditory-Tactile Stimuli

2020 ◽  
Vol 10 (8) ◽  
pp. 524
Author(s):  
Boyang Zhang ◽  
Zongtan Zhou ◽  
Jing Jiang
Keyword(s):  
Information Transfer ◽  
Transfer Functions ◽  
Brain Computer Interface ◽  
Target Selection ◽  
Poor Performance ◽  
Event Related Potential ◽  
Computer Interface ◽  
Online Information ◽  
Linear Discriminant ◽  
Tactile Stimuli

To date, traditional visual-based event-related potential brain-computer interface (ERP-BCI) systems continue to dominate the mainstream BCI research. However, these conventional BCIs are unsuitable for the individuals who have partly or completely lost their vision. Considering the poor performance of gaze independent ERP-BCIs, it is necessary to study techniques to improve the performance of these BCI systems. In this paper, we developed a novel 36-class bimodal ERP-BCI system based on tactile and auditory stimuli, in which six-virtual-direction audio files produced via head related transfer functions (HRTF) were delivered through headphones and location-congruent electro-tactile stimuli were simultaneously delivered to the corresponding position using electrodes placed on the abdomen and waist. We selected the eight best channels, trained a Bayesian linear discriminant analysis (BLDA) classifier and acquired the optimal trial number for target selection in online process. The average online information transfer rate (ITR) of the bimodal ERP-BCI reached 11.66 bit/min, improvements of 35.11% and 36.69% compared to the auditory (8.63 bit/min) and tactile approaches (8.53 bit/min), respectively. The results demonstrate the performance of the bimodal system is superior to each unimodal system. These facts indicate that the proposed bimodal system has potential utility as a gaze-independent BCI in future real-world applications.

Simultaneous Classification of Multiple Motor Imagery and P300 for Increase in Output Information of Brain-computer Interface

2013 ◽  
Vol 133 (3) ◽  
pp. 635-641
Author(s):  
Genzo Naito ◽  
Lui Yoshida ◽  
Takashi Numata ◽  
Yutaro Ogawa ◽  
Kiyoshi Kotani ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Output Information

NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

2019 ◽  
pp. 139-145
Author(s):  
Selma Büyükgöze
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Electrode Placement ◽  
Eeg Signals ◽  
Non Invasive ◽  
Brain Signals ◽  
Brain States ◽  
The Brain

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

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