Three-Dimensional Brain–Computer Interface Control Through Simultaneous Overt Spatial Attentional and Motor Imagery Tasks

Meng, Jianjun; Streitz, Taylor D.; Gulachek, Nicholas; Suma, Daniel; He, Bin

doi:10.1109/tbme.2018.2872855

Cited by 47 publications

(38 citation statements)

References 60 publications

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“…1) was applied, which had successfully been applied in some previous research. [15][16][17][18][19][20][21] It is an ultra-portable device (320 x 45 x 40 mm, 145 g) that can move 25 cm horizontally, 11 cm vertically and 15 cm in-depth; it can be fitted on the monitor and uses infra camera observation and image procession to detect and follow eye movement with 60 Hz sample rate. It is easy to handle with 0.5-1 degree of visual angle and 5-or 9 point-calibration options.…”

Section: The Gazepointgp3 Eye-tracker Hardware Unitmentioning

confidence: 99%

Evaluation of Eye-Movement Metrics ina Software DebbugingTask using GP3 Eye Tracker

Kővári¹,

Katona²,

Costescu³

2020

ACTA POLYTECH HUNG

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Teaching different programming subjects is an increasing challenge nowadays as because of growing user demands, the latest paradigms and technologies must be taught. Students in higher education now are of the age who were born into the digital world; however, the success of fulfilling programming courses is lagging behind. Humancomputer interface-based research has emerged in numerous fields of science recently, which could lead to the revolutionising of education. These interfaces could also help professors as a support system in transferring knowledge in a more efficient way besides supporting students acquiring adequate home learning methods. In this study, the applicability of eye movement tracking systems in respect of a programming task is examined, in which during the exploration and correction of the errors of an incorrectly functioning algorithm, the eye movement parameters are observed, recorded and evaluated. The test subjects participating in the research were divided into two groups according to some of their characteristic parameters, where the first group during debugging rather applied minor modifications and the more common technique of compile and run, which otherwise is also the most characteristic feature of students studying programming, while the members of the other group increased emphasis on analysing. In the statistic evaluation of the research, the parameters characteristic of the eye movement tracking of the two groups, as well as the efficiency of these groups were analysed. Based on the results, regarding the efficiency and the number of fixations, a significant difference could be shown between the two groups (U=22.5, Z=-2.236, p=0.025 (2-tailed), r=0.48) and (t(20)=2.507, p=0.021 (2-tailed), d=1.106), while concerning the duration of fixation and the saccade length, the difference shown was infinitesimal (t(20)=0.544, p=0.592 (2tailed), d=0.26) and (t(19.992)=1.965, p=0.063 (2-tailed), d=0.79).

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Section: The Gazepointgp3 Eye-tracker Hardware Unitmentioning

confidence: 99%

Evaluation of Eye-Movement Metrics ina Software DebbugingTask using GP3 Eye Tracker

Kővári¹,

Katona²,

Costescu³

2020

ACTA POLYTECH HUNG

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“…The SMR or mu rhythm in EEG is generated by the synchronized electrical brain activity over the motor cortex area and has a frequency range of around 8–12 Hz ( Pfurtscheller et al, 2006 ; Bernier et al, 2007 ). In BCI applications, the frequency band centered at 12 Hz ( Royer et al, 2010 ; Doud et al, 2011 ; Cassady et al, 2014 ; Meng et al, 2016 , 2018 ; Stieger et al, 2020 ) was shown to be effective in SMR control. Event-related desynchronization (ERD) occurs when the amplitude of mu rhythm decreases in response to a person moving or imagining moving her/his body ( Pfurtscheller and Aranibar, 1979 ).…”

Section: Introductionmentioning

confidence: 99%

Effects of Long-Term Meditation Practices on Sensorimotor Rhythm-Based Brain-Computer Interface Learning

et al. 2021

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Sensorimotor rhythm (SMR)-based brain–computer interfaces (BCIs) provide an alternative pathway for users to perform motor control using motor imagery. Despite the non-invasiveness, ease of use, and low cost, this kind of BCI has limitations due to long training times and BCI inefficiency—that is, the SMR BCI control paradigm may not work well on a subpopulation of users. Meditation is a mental training method to improve mindfulness and awareness and is reported to have positive effects on one’s mental state. Here, we investigated the behavioral and electrophysiological differences between experienced meditators and meditation naïve subjects in one-dimensional (1D) and two-dimensional (2D) cursor control tasks. We found numerical evidence that meditators outperformed control subjects in both tasks (1D and 2D), and there were fewer BCI inefficient subjects in the meditator group. Finally, we also explored the neurophysiological difference between the two groups and showed that the meditators had a higher resting SMR predictor, more stable resting mu rhythm, and a larger control signal contrast than controls during the task.

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“…Most of the BCI systems are EEG-based due to its relatively low expense, high portability, better time resolution, and minimal risks to users as compared to other modalities (Arvaneh et al, 2011 ). At present, the most extensively used brain signals for BCI input are event-related potential (ERP) (Zhang et al, 2014 ; Jin et al, 2015 , 2017 ; Yin et al, 2015 , 2016 ; Xu et al, 2018 ), steady-state visual evoked potential (SSVEP) (Pan et al, 2013 ; Wang et al, 2016 ; Xing et al, 2018 ; Zhang et al, 2018 ), and motor-imagery (MI) (Zhang et al, 2015 , 2017 ; Ang and Guan, 2017 ; Qiu et al, 2017 ; Meng et al, 2018 ; Lugo et al, 2019 ). MI-BCI works without outer stimuli, thus it is more intuitive for users (Yu et al, 2012 ; Velasco-Alvarez et al, 2013 ).…”

Section: Introductionmentioning

confidence: 99%

Temporal Combination Pattern Optimization Based on Feature Selection Method for Motor Imagery BCIs

Jiang

Wang

et al. 2020

Front. Hum. Neurosci.

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Common spatial pattern (CSP) method is widely used for spatial filtering and brain pattern extraction from electroencephalogram (EEG) signals in motor imagery (MI)-based brain-computer interfaces (BCIs). The participant-specific time window relative to the visual cue has a significant impact on the effectiveness of the CSP. However, the time window is usually selected experientially or manually. To solve this problem, we propose a novel feature selection approach for MI-based BCIs. Specifically, multiple time segments were obtained by decomposing each EEG sample of the MI task. Furthermore, the features were extracted by CSP from each time segment and were combined to form a new feature vector. Finally, the optimal temporal combination patterns for the new feature vector were selected based on four feature selection algorithms, i.e., mutual information, least absolute shrinkage and selection operator, principal component analysis and stepwise linear discriminant analysis (denoted as MUIN, LASSO, PCA, and SWLDA, respectively), and the classification algorithm was employed to evaluate the average classification accuracy. With three BCI competition datasets, the results of the four proposed algorithms were compared with traditional CSP algorithm in classification accuracy. Experimental results show that compared with traditional algorithm, the proposed methods significantly improve performance. Specifically, the LASSO achieved the highest accuracy (88.58%) among the proposed methods. Importantly, the average classification accuracies using the proposed approaches significantly improved 10.14% (MUIN), 11.40% (LASSO), 6.08% (PCA), and 10.25% (SWLDA) compared to that using CSP. These results indicate that the proposed approach is expected to be practical in MI-based BCIs.

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Three-Dimensional Brain–Computer Interface Control Through Simultaneous Overt Spatial Attentional and Motor Imagery Tasks

Cited by 47 publications

References 60 publications

Evaluation of Eye-Movement Metrics ina Software DebbugingTask using GP3 Eye Tracker

Evaluation of Eye-Movement Metrics ina Software DebbugingTask using GP3 Eye Tracker

Effects of Long-Term Meditation Practices on Sensorimotor Rhythm-Based Brain-Computer Interface Learning

Temporal Combination Pattern Optimization Based on Feature Selection Method for Motor Imagery BCIs

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