Personalized Brain–Computer Interface and Its Applications

Ma, Yixin; Gong, Anmin; Nan, Wenya; Ding, Peng; Wu, Fan; Fu, Yunfa

doi:10.3390/jpm13010046

Cited by 20 publications

(14 citation statements)

References 105 publications

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“…In order to control the robot, we designed novel user BCIs, trialling both visual and auditory modalities. Users displayed preferences for different interfaces, highlighting the importance of personalisation in BCI design ( Ma et al, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

“…We then perform a user study, using the language-conditioned Perceiver-Actor ( Shridhar et al, 2022b ) model to control a robot arm to perform a multi-stage task, cleaning up a table. We find that users prefer different interfaces, and hence personalised BCIs ( Ma et al, 2022 ) should be considered when developing future BCI + robot systems.…”

Section: Introductionmentioning

confidence: 98%

See 1 more Smart Citation

A comparison of visual and auditory EEG interfaces for robot multi-stage task control

Arulkumaran,

Di Vincenzo,

Dossa

et al. 2024

Front. Robot. AI

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Shared autonomy holds promise for assistive robotics, whereby physically-impaired people can direct robots to perform various tasks for them. However, a robot that is capable of many tasks also introduces many choices for the user, such as which object or location should be the target of interaction. In the context of non-invasive brain-computer interfaces for shared autonomy—most commonly electroencephalography-based—the two most common choices are to provide either auditory or visual stimuli to the user—each with their respective pros and cons. Using the oddball paradigm, we designed comparable auditory and visual interfaces to speak/display the choices to the user, and had users complete a multi-stage robotic manipulation task involving location and object selection. Users displayed differing competencies—and preferences—for the different interfaces, highlighting the importance of considering modalities outside of vision when constructing human-robot interfaces.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

A comparison of visual and auditory EEG interfaces for robot multi-stage task control

Arulkumaran,

Di Vincenzo,

Dossa

et al. 2024

Front. Robot. AI

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show abstract

“…192 In one survey, 63% of patients used high-tech assistive devices as their main way of communication. 19 Personalized BCIs 193 and even controlling a humanoid robot 194 or exoskeleton 195 with BCI are investigated. However, BCIs have some usability challenges as a lot of time is needed for training, the usage can be very fatiguing and restricted to special settings.…”

Section: Treatmentmentioning

confidence: 99%

Locked-in syndrome revisited

Schnetzer

McCoy

Bergmann

et al. 2023

Ther Adv Neurol Disord

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The locked-in syndrome (LiS) is characterized by quadriplegia with preserved vertical eye and eyelid movements and retained cognitive abilities. Subcategorization, aetiologies and the anatomical foundation of LiS are discussed. The damage of different structures in the pons, mesencephalon and thalamus are attributed to symptoms of classical, complete and incomplete LiS and the locked-in plus syndrome, which is characterized by additional impairments of consciousness, making the clinical distinction to other chronic disorders of consciousness at times difficult. Other differential diagnoses are cognitive motor dissociation (CMD) and akinetic mutism. Treatment options are reviewed and an early, interdisciplinary and aggressive approach, including the provision of psychological support and coping strategies is favoured. The establishment of communication is a main goal of rehabilitation. Finally, the quality of life of LiS patients and ethical implications are considered. While patients with LiS report a high quality of life and well-being, medical professionals and caregivers have largely pessimistic perceptions. The negative view on life with LiS must be overthought and the autonomy and dignity of LiS patients prioritized. Knowledge has to be disseminated, diagnostics accelerated and technical support system development promoted. More well-designed research but also more awareness of the needs of LiS patients and their perception as individual persons is needed to enable a life with LiS that is worth living.

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“…However, it is difficult to adapt to individual difference because individual difference is related to some internal factors, including personality traits, brain structure, psychological, and physiological states, etc [11]. Myrden and Chau [12] demonstrated through a maze navigation game that subjects' psychological states affected EEG-BCI performance, such as fatigue, frustration, and attention.…”

Section: Introductionmentioning

confidence: 99%

Personalized motor imagery prediction model based on individual difference of ERP

Deng,

Li,

Zuo

et al. 2024

J. Neural Eng.

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Objective. Motor imagery-based brain-computer interaction (MI-BCI) is a novel method of achieving human and external environment interaction that can assist individuals with motor disorders to rehabilitate. However, individual differences limit the utility of the MI-BCI. In this study, a personalized MI prediction model based on the individual difference of event-related potential (ERP) is proposed to solve the MI individual difference. Approach. A novel paradigm named action observation-based multi-delayed matching posture task (AO-multi-DMPT) evokes ERP during a DMPT phase by retrieving picture stimuli and videos, and generates MI EEG through action observation and autonomous imagery in an AO-MI phase. Based on the correlation between the ERP and MI, a logistic regression-based personalized MI prediction model is built to predict each individual‘s suitable MI action. 32 subjects conducted the MI task with or without the help of the prediction model to select the MI action. Then classification accuracy of the MI task is used to evaluate the proposed model and three traditional MI methods. Main results. The personalized MI prediction model successfully predicts suitable action among 3 sets of daily actions. Under suitable MI action, the individual’s ERP amplitude and ERD intensity are the largest, which helps to improve the accuracy by 14.25%. Significance. The personalized MI prediction model that uses the temporal ERP features to predict the classification accuracy of MI is feasible for improving the individual’s MI-BCI performance, providing a new personalized solution for the individual difference and practical BCI application.

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Personalized Brain–Computer Interface and Its Applications

Cited by 20 publications

References 105 publications

A comparison of visual and auditory EEG interfaces for robot multi-stage task control

A comparison of visual and auditory EEG interfaces for robot multi-stage task control

Locked-in syndrome revisited

Personalized motor imagery prediction model based on individual difference of ERP

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