Effectiveness of subject specific instruction on mu-based brain-computer interface training

Battison, Alexandria; Schlussel, Maura; Fuller, Tom; Yu, Yih‐Choung; Gabel, Lisa A.

doi:10.1109/nebec.2015.7117148

Cited by 6 publications

(13 citation statements)

References 3 publications

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“…The context might potentially be an important guidance parameter. For example, in [167], subjects in the "participant specific" task group were asked to describe a hand motor task they typically preform, i.e. a preferred movement or a familiar movement from daily activity, and then asked to bring an object associated with the chosen task, which was placed in front of the participants to aid them in their imagination of this specific movement.…”

Section: Contextmentioning

confidence: 99%

“…We do advise if possible to conduct screening (usercentered task selection) with a set of tasks, including motor imagery and cognitive tasks [37,78,131]. It is also recommended to activate prior knowledge instead of asking the user to perform an unfamiliar task [228], possibly to instruct complex tasks [147] and to use specific rather than a vague instruction [167]. Finally, although the nature of appropriate guidance is still unclear and might depend on users' individual characteristics, one aspect which received some attention is the focus in motor imagery, where it seems that kinesthetic imagery is better than purely visual imagery [42].…”

Section: Guidelines For the Instructions In Mt-bci Trainingmentioning

confidence: 99%

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A review of user training methods in brain computer interfaces based on mental tasks

et al. 2021

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Mental-Tasks based Brain-Computer Interfaces (MT-BCIs) allow their users to interact with an external device solely by using brain signals produced through mental tasks. While MT-BCIs are promising for many applications, they are still barely used outside laboratories due to their lack of reliability. MT-BCIs require their users to develop the ability to self-regulate specific brain signals. However, the human learning process to control a BCI is still relatively poorly understood and how to optimally train this ability is currently under investigation. Despite their promises and achievements, traditional training programs have been shown to be sub-optimal and could be further improved. In order to optimize user training and improve BCI performance, human factors should be taken into account. An interdisciplinary approach should be adopted to provide learners with appropriate and/or adaptive training. In this article, we provide an overview of existing methods for MT-BCI user training-notably in terms of environment, instructions, feedback and exercises. We present a categorization and taxonomy of these training approaches, provide guidelines on how to choose the best methods and identify open challenges and perspectives to further improve MT-BCI user training.

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Section: Contextmentioning

confidence: 99%

Section: Guidelines For the Instructions In Mt-bci Trainingmentioning

confidence: 99%

A review of user training methods in brain computer interfaces based on mental tasks

et al. 2021

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“…Another possible explanation for this difference between cognitive tasks may be the lack of precision and the subjectivity of the instructions of the worst performing task. That hypothesis follows the lines of Corbit, Gabel, and Yu, 2013;and Battison, Schlussel, Fuller, Yu, and Gabel, 2015, in which they defend the importance of using specific and well-defined instructions to obtain an adequate control of a SMR-based BCI.…”

Section: Discussionmentioning

confidence: 73%

A Shaping Procedure to Modulate Two Cognitive Tasks to Improve a Sensorimotor Rhythm-Based Brain-Computer Interface System

et al. 2018

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This study aimed to propose an adapted feedback using a psychological learning technique based on Skinner’s shaping method to help the users to modulate two cognitive tasks (right-hand motor imagination and relaxed state) and improve better control in a Brain-Computer Interface. In the first experiment, a comparative study between performance in standard feedback (N = 9) and shaping method (N = 10) was conducted. The NASA Task Load Index questionnaire was applied to measure the user’s workload. In the second experiment, a single case study was performed (N = 5) to verify the continuous learning by the shaping method. The first experiment showed significant interaction effect between sessions and group (F(1, 17) = 5.565; p = .031) which the shaping paradigm was applied. A second interaction effect demonstrates a higher performance increase in the relax state task with shaping procedure (F(1, 17) = 5. 038; p = .038). In NASA-TXL an interaction effect was obtained between the group and the cognitive task in Mental Demand (F(1, 17) = 6, 809; p = .018), Performance (F(1, 17) = 5, 725; p = .029), and Frustration (F(1, 17) = 9, 735; p = .006), no significance was found in Effort. In the second experiment, a trial-by-trial analysis shows an ascendant trend learning curve for the cognitive task with the lowest initial acquisition (relax state). The results suggest the effectiveness of the shaping procedure to modulate brain rhythms, improving mainly the cognitive task with greater initial difficulty and provide better interaction perception.

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“…Algunos de los avances en este tema han sido; por un lado, la propuesta de dife-rentes métodos para clasificar a los individuos como usuarios de alta o baja aptitud y de este modo evitar el consumo de tiempo adaptando a un usuario que probablemente requiera otro tipo de entrenamiento y/o sistema. Por otro lado, se ha mencionado en recientes investigaciones que el desempeño de un sistema ICC puede mejorar, con el simple hecho de proporcionar al usuario instrucciones específicas sobre las tareas de control a desempeñar [15] . En base a lo anteriormente expuesto, en el presente artículo de revisión se busca: (1) comprender a fondo la estructura de una ICC y los tipos de sistema (Conceptualización), (2) analizar el reto que tiene la comunidad científica para mejorar la interacción entre usuario y sistema desde la perspectiva de la ingeniería de factores humanos (Retos de Rediseño) y (3) describir la aplicación de este tipo de tecnología de asistencia en desarrollo en la sociedad mexicana (Impacto Social).…”

Section: Interacción Entre El Usuario Y El Sistemaunclassified

“…También se ha sugerido que el entrenamiento basado en dos estados cognitivos básicos, movimiento imaginario (ERD) y relajación (ERS), mejoran en su totalidad la presencia de la onda μ [33] . Todavía más, de acuerdo a [15] , entre más específica sea la tarea motora imaginaria, mayor será la potencia de la onda μ.…”

Section: Sincronización Y Desincronización Relacionadas a Eventos (Erd/ers)unclassified

Interfaces Cerebro-Computadora: Conceptualización, Retos de Rediseño e Impacto Social

Alonso-Valerdi

Arreola-Villarruel

Argüello-García

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Las Interfaces Cerebro-Computadora (ICCs) son sistemas que miden la actividad del Sistema Nervioso Central y la convierten en salidas que reemplazan, restauran, aumentan, suplementan o mejoran las salidas naturales de dicho sistema y, por lo tanto, cambian las interacciones en curso entre el ser humano y su ambiente interno o externo. Sin embargo, a más de medio siglo de ser investigadas en condiciones de laboratorio, las ICC no han podido ser trasla- dadas al mundo real. En el presente artículo de revisión se busca: (1) comprender a fondo la estructura de una ICC y los tipos de sistema, (2) analizar el reto que tiene la comunidad científica para mejorar la interacción entre usua- rio y sistema desde la perspectiva de la ingeniería de factores humanos y (3) describir la aplicación de este tipo de tecnología de asistencia en desarrollo en la sociedad mexicana. El futuro de estas ICC y la eficiencia con que logren el objetivo para el que fueron diseñadas, parece depender más que nunca de factores relacionados a la percepción subjetiva del usuario, su adaptación al manejo de las ICC y el proceso de internalizarlas como propias en su espacio personal y su psique.

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Effectiveness of subject specific instruction on mu-based brain-computer interface training

Cited by 6 publications

References 3 publications

A review of user training methods in brain computer interfaces based on mental tasks

A review of user training methods in brain computer interfaces based on mental tasks

A Shaping Procedure to Modulate Two Cognitive Tasks to Improve a Sensorimotor Rhythm-Based Brain-Computer Interface System

Interfaces Cerebro-Computadora: Conceptualización, Retos de Rediseño e Impacto Social

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