Are Online Co-adaptive Sensorimotor Rhythm Brain-Computer Interface Training Paradigms Effective?

Cunha, Jose Diogo; Scherer, Reinhold

doi:10.1109/cw.2018.00081

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…Compumedics Neuroscan [ 67 , 68 , 69 ], and Emotiv [ 70 , 71 , 72 ] equipment was employed in three articles each. Two articles used OpenBCI equipment [ 73 , 74 ] and one article each used the equipment of Biosemi [ 75 ], g.Tec [ 76 ], ANT Neuro [ 77 ], Brain Products [ 78 ], and Advance Brain Monitoring [ 79 ].…”

Section: Resultsmentioning

confidence: 99%

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

Jamil

Belkacem

Ouhbi

et al. 2021

Sensors

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Humans interact with computers through various devices. Such interactions may not require any physical movement, thus aiding people with severe motor disabilities in communicating with external devices. The brain–computer interface (BCI) has turned into a field involving new elements for assistive and rehabilitative technologies. This systematic literature review (SLR) aims to help BCI investigator and investors to decide which devices to select or which studies to support based on the current market examination. This examination of noninvasive EEG devices is based on published BCI studies in different research areas. In this SLR, the research area of noninvasive BCIs using electroencephalography (EEG) was analyzed by examining the types of equipment used for assistive, adaptive, and rehabilitative BCIs. For this SLR, candidate studies were selected from the IEEE digital library, PubMed, Scopus, and ScienceDirect. The inclusion criteria (IC) were limited to studies focusing on applications and devices of the BCI technology. The data used herein were selected using IC and exclusion criteria to ensure quality assessment. The selected articles were divided into four main research areas: education, engineering, entertainment, and medicine. Overall, 238 papers were selected based on IC. Moreover, 28 companies were identified that developed wired and wireless equipment as means of BCI assistive technology. The findings of this review indicate that the implications of using BCIs for assistive, adaptive, and rehabilitative technologies are encouraging for people with severe motor disabilities and healthy people. With an increasing number of healthy people using BCIs, other research areas, such as the motivation of players when participating in games or the security of soldiers when observing certain areas, can be studied and collaborated using the BCI technology. However, such BCI systems must be simple (wearable), convenient (sensor fabrics and self-adjusting abilities), and inexpensive.

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Section: Resultsmentioning

confidence: 99%

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

Jamil

Belkacem

Ouhbi

et al. 2021

Sensors

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show abstract

“…Except for the first 10 trials of the first run that were used to calibrate the initial instance of the BCI model, users were in closed-loop control of the interface and observing real-time feedback throughout the training session. The users' task was to play a version of the "Whack-A-Mole" game [53] where timely, successful and sustained MI would "hammer" and knock out a cartoon ghost character leading to a "hit" trial and collection of "stars" (virtual rewards). The game-like design targeted high levels of user motivation and engagement to facilitate coadaptation [54], [55].…”

Section: Introductionmentioning

confidence: 99%

“…(b) Game graphical user interface associated to the trial structure and timeline. Image adopted from[53]. (c) Training paradigm structure.…”

mentioning

confidence: 99%

Post-Adaptation Effects in a Motor Imagery Brain-Computer Interface Online Coadaptive Paradigm

et al. 2021

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Online coadaptive training has been successfully employed to enable people to control motor imagery (MI)-based brain-computer interfaces (BCIs), allowing to completely skip the lengthy and demotivating open-loop calibration stage traditionally applied before closed-loop control. However, practical reasons may often dictate to eventually switch off decoder adaptation and proceed with BCI control under a fixed BCI model, a situation that remains rather unexplored. This work studies the existence and magnitude of potential post-adaptation effects on system performance, subject learning and brain signal modulation stability in a state-of-the-art, coadaptive training regime inspired by a game-like design. The results extracted in a cohort of 20 able-bodied individuals reveal that ceasing classifier adaptation after three runs (approx. 30 min) of a single-session training protocol had no significant impact on any of the examined BCI control and learning aspects in the remaining two runs (about 20 min) with a fixed classifier. Fifteen individuals achieved accuracies that are better than chance level and allowed them to successfully execute the given task. These findings alleviate a major concern regarding the applicability of coadaptive MI BCI training, thus helping to further establish this training approach and allow full exploitation of its benefits.

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Direct comparison of supervised and semi-supervised retraining approaches for co-adaptive BCIs

Schwarz

Brandstetter

Pereira

et al. 2019

Med Biol Eng Comput

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For Brain-Computer interfaces (BCIs), system calibration is a lengthy but necessary process for successful operation. Co-adaptive BCIs aim to shorten training and imply positive motivation to users by presenting feedback already at early stages: After just 5 min of gathering calibration data, the systems are able to provide feedback and engage users in a mutual learning process. In this work, we investigate whether the retraining stage of co-adaptive BCIs can be adapted to a semi-supervised concept, where only a small amount of labeled data is available and all additional data needs to be labeled by the BCI itself. The aim of the current work was to evaluate whether a semi-supervised co-adaptive BCI could successfully compete with a supervised co-adaptive BCI model. In a supporting two-class (190 trials per condition) BCI study based on motor imagery tasks, we evaluated both approaches in two separate groups of 10 participants online, while we simulated the other approach in each group offline. Our results indicate that despite the lack of true labeled data, the semi-supervised driven BCI did not perform significantly worse (p > 0.05) than the supervised counterpart. We believe that these findings contribute to developing BCIs for long-term use, where continuous adaptation becomes imperative for maintaining meaningful BCI performance. Graphical abstractIn this work, we investigate whether the retraining stage of a co-adaptive BCI can be adapted to a semi-supervised concept, where only a small amount of labeled data is available and all additional data needs to be labeled by the BCI itself. In two groups of 10 persons, we evaluate a supervised as well as a semi-supervised approach. Our results indicate that despite the lack of true labeled data, the semi-supervised driven BCI did not perform significantly worse (p > 0.05) than the supervised counterpart. Electronic supplementary materialThe online version of this article (10.1007/s11517-019-02047-1) contains supplementary material, which is available to authorized users.

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Are Online Co-adaptive Sensorimotor Rhythm Brain-Computer Interface Training Paradigms Effective?

Cited by 3 publications

References 16 publications

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

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

Post-Adaptation Effects in a Motor Imagery Brain-Computer Interface Online Coadaptive Paradigm

Direct comparison of supervised and semi-supervised retraining approaches for co-adaptive BCIs

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