First study towards linear control of an upper-limb neuroprosthesis with an EEG-based Brain-Computer Interface

Pascual, Javier; Velasco-Álvarez, Francisco; Müller, Klaus‐Robert; Vidaurre, Carmen

doi:10.1109/embc.2012.6346663

Cited by 6 publications

(5 citation statements)

References 20 publications

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“…Real time studies proves that performance of the method obtain major statistical improvement to control wheelchair in more natural way. From the Table.4 and Table .5 we interpret that compared to previous study [14]- [20], [26]- [28], [35]- [37], [39], [41]- [44] our system outperformance in terms of accuracy, user friendly and less training with sophisticated design. From the online analysis, Subject S1, S3 and S4 performance was appreciated compared with S2.…”

Section: Real-time Experimentssupporting

confidence: 59%

“…Reshmi and Amal [38] designed EEG based wheelchair to control five different states using wavelet coefficients and Support Vector Machine [47]. Pascual et al [39] modeled non-invasive BCI using Motor Imagery tasks to controlled neuroprosthesis with the help of neuromuscular electrical stimulation and obtained 80% accuracy for two imagery classes using single trail analysis. From these surveys we concluded that Brain computer Interface using Electroencephalogram is possible.…”

Section: Literature Surveymentioning

confidence: 99%

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Brain Computer Interface for Neurodegenerative Person Using Electroencephalogram

Ramkumar

Emayavaramban

et al. 2019

IEEE Access

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Brain-computer interface (BCI) connects the outside world, in real time and in a natural way, like biological communication system. It facilitates the communication link from the brain to the external world by converting brain thoughts in to control commands to control the external devices, such as wheelchair, keyboard mouse, and other home appliances. Measuring the electrical brain activity by placing electrodes over scalp is called electroencephalogram (EEG). By combining these two techniques, we are able to create EEG-based BCI. In this paper, we use band power and radial basis function to analyze the signal for four mentally composed tasks to design four states BCI for a neurodegenerative person using EEG. Online study was conducted to analyze the performance of the wheelchair for a neurodegenerative person. The result shows that an overall average classification accuracy of 92.50% and individual tasks with an average classification of 95%, 87.50%, 92.50%, and 95.00% were achieved for the four tasks. The result proves that control commands generated from the EEG signal have the bcapacity to control the intelligent systems.INDEX TERMS Brain computer interface, band power, radial basis function, FRDM-KL25Z.

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Section: Real-time Experimentssupporting

confidence: 59%

Section: Literature Surveymentioning

confidence: 99%

Brain Computer Interface for Neurodegenerative Person Using Electroencephalogram

Ramkumar

Emayavaramban

et al. 2019

IEEE Access

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“…However, it should be noted that the users did not actually operate a neuroprosthesis, which severely limits the external validity of the results. Pascual et al [63] reported 'remarkable' differences in the experience of the users controlling an upper-limb neuromuscular electrical stimulation (NMES) neuroprosthesis. For some users, the disturbance of the NMES apparently impaired their ability to concentrate, while others perceived the stimulation as 'reinforcement' feedback.…”

Section: Discussionmentioning

confidence: 99%

Towards a holistic assessment of the user experience with hybrid BCIs

Lorenz¹,

Pascual²,

Blankertz³

et al. 2014

J. Neural Eng.

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Although the paramount goal in BCI research still remains the improvement of classification accuracy and communication speed, it is of significance to note that it is equally important for end-users to keep up their motivation and prevent frustration. By including pragmatic as well as hedonic quality aspects, this study is the first effort to gain a holistic perspective of the UX while interacting with BCI-driven assistive technology aimed at actual end-users. The broad-scale methodology provided valuable insights into the underlying dynamics causing the users' experience to differ across the GUIs. The results will be used to refine a BCI-driven neuroprosthesis and test it with end-users.

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“…The electroencephalogram (EEG) offers the potential to examine cognitive effort with precise temporal resolution and freedom of movement during data collection, facilitating adaptability to clinical, operational, or real-world settings [24] , [25] , [26] , [27] . Remarkably, although efforts to use measures of cortical dynamics such as EEG are increasingly abundant in the literature for control of assistive devices [28] , [29] , [30] , [31] , [32] , [33] , [34] , EEG measures of cognitive workload for evaluation of new HMI technologies have not been adapted and applied to this field.…”

Section: Introductionmentioning

confidence: 99%

A Simple ERP Method for Quantitative Analysis of Cognitive Workload in Myoelectric Prosthesis Control and Human-Machine Interaction

et al. 2014

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Common goals in the development of human-machine interface (HMI) technology are to reduce cognitive workload and increase function. However, objective and quantitative outcome measures assessing cognitive workload have not been standardized for HMI research. The present study examines the efficacy of a simple event-related potential (ERP) measure of cortical effort during myoelectric control of a virtual limb for use as an outcome tool. Participants trained and tested on two methods of control, direct control (DC) and pattern recognition control (PRC), while electroencephalographic (EEG) activity was recorded. Eighteen healthy participants with intact limbs were tested using DC and PRC under three conditions: passive viewing, easy, and hard. Novel auditory probes were presented at random intervals during testing, and significant task-difficulty effects were observed in the P200, P300, and a late positive potential (LPP), supporting the efficacy of ERPs as a cognitive workload measure in HMI tasks. LPP amplitude distinguished DC from PRC in the hard condition with higher amplitude in PRC, consistent with lower cognitive workload in PRC relative to DC for complex movements. Participants completed trials faster in the easy condition using DC relative to PRC, but completed trials more slowly using DC relative to PRC in the hard condition. The results provide promising support for ERPs as an outcome measure for cognitive workload in HMI research such as prosthetics, exoskeletons, and other assistive devices, and can be used to evaluate and guide new technologies for more intuitive HMI control.

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First study towards linear control of an upper-limb neuroprosthesis with an EEG-based Brain-Computer Interface

Cited by 6 publications

References 20 publications

Brain Computer Interface for Neurodegenerative Person Using Electroencephalogram

Brain Computer Interface for Neurodegenerative Person Using Electroencephalogram

Towards a holistic assessment of the user experience with hybrid BCIs

A Simple ERP Method for Quantitative Analysis of Cognitive Workload in Myoelectric Prosthesis Control and Human-Machine Interaction

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