Real-time neurofeedback is effective in reducing diversion of attention from a motor task in healthy individuals and patients with amyotrophic lateral sclerosis

Abstract

Objective. The performance of brain-computer interface (BCI) systems is influenced by the user’s mental state, such as attention diversion. In this study, we propose a novel online BCI system able to adapt with variations in the users’ attention during real-time movement execution. Approach. Electroencephalography signals were recorded from healthy participants and patients with Amyotrophic Lateral Sclerosis while attention to the target task (a dorsiflexion movement) was drifted using an auditory oddball task. For each participant, the selected channels, classifiers and features from a training data set were used in the online phase to predict the attention status. Main results. For both healthy controls and patients, feedback to the user on attentional status reduced the amount of attention diversion. Significance. The findings presented here demonstrate successful monitoring of the users’ attention in a fully online BCI system, and further, that real-time neurofeedback on the users’ attention state can be implemented to focus the attention of the user back onto the main task.

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review

“…Medical : This is the most popular research area in this SLR, with 193 articles. g.Tec equipment was used in 54 articles for the EEG method [ 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 ], 33 articles used Emotiv equipment [ ...…”

Participant-specific classifier tuning increases the performance of hand movement detection from EEG in patients with amyotrophic lateral sclerosis

“…To obtain the spectral features, raw EEG signals were band-pass filtered in theta (4-8 Hz), alpha (8)(9)(10)(11)(12)(13), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and gamma (30-80) bands using a zero phase second order Butterworth filter, and then squared. The filtered signals were then segmented into movement and rest epochs, and the mean was calculated over the same sliding windows as for the temporal features to estimate the average power.…”

“…This technique outperformed movement detection using template matching with raw EEG samples [21]. Another approach to constructing temporal features is to divide the signal segment into windows, with or without overlap, and then compute average parameters, such as the mean, slope and variability [23][24][25]. Temporal parameters extracted from an MRCP can be supplemented with spectral features capturing the change in power within relevant frequency bands [18,26].…”

Standardization of protocol design for user training in EEG-based Brain-Computer Interface

“…Adaptive interaction. The interface (task design) can adapt to its users, as the difficulty that was gradually, within sessions, increased to improve user learning, from 1D, 2D to 3D control [80], while the speed increased difficulty within trials [81]; or the number of flashes reduced in each sequence [82]; also, the difficulty varied in real-time to increase flow state [58]; or interaction 'froze' when high attention level was detected [83]; and attention diversion during motor execution was regulated with auditory oddball paradigm [84]. Users' skills can be assessed (before interaction) in order to design adapted BCI tasks and increase performance [85].…”