An Introductory Tutorial on Brain–Computer Interfaces and Their Applications

Bonci, Andrea; Fiori, Simone; Hara, Hiroshi; Tanaka, Tomomi; Verdini, Federica

doi:10.3390/electronics10050560

Cited by 36 publications

(27 citation statements)

References 264 publications

(332 reference statements)

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“…Some considerations about the limitations and challenges related to the BCI usage and applications are revealed even before moving to the embedded implementation of BCI technology [ 10 , 83 ]. Such limitations we found are: Inaccuracy of the BCI in terms of prediction or classifying brain signals.…”

Section: Challenges and Future Research Directions For Ebci Systemsmentioning

confidence: 99%

“…For the other one, it contends to place sensors on the scalp and recovers cerebral activity by measuring the electrical activity (EEG), Functional magnetic resonance imaging (fMRI), and magnetic activity (MEG). The noninvasive technique based on EEG method remains the most solicited for several decades due to its ease of application, low cost, good temporal resolution and not requiring surgical operation [ 10 ]. Furthermore, such acquisition systems are based on very accurately analog-to-digital converter (DAC) allowing for the capture of a very small variation in the signal.…”

Section: Introductionmentioning

confidence: 99%

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Embedded Brain Computer Interface: State-of-the-Art in Research

Belwafi

Gannouni

Aboalsamh

2021

Sensors

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There is a wide area of application that uses cerebral activity to restore capabilities for people with severe motor disabilities, and actually the number of such systems keeps growing. Most of the current BCI systems are based on a personal computer. However, there is a tremendous interest in the implementation of BCIs on a portable platform, which has a small size, faster to load, much lower price, lower resources, and lower power consumption than those for full PCs. Depending on the complexity of the signal processing algorithms, it may be more suitable to work with slow processors because there is no need to allow excess capacity of more demanding tasks. So, in this review, we provide an overview of the BCIs development and the current available technology before discussing experimental studies of BCIs.

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Section: Challenges and Future Research Directions For Ebci Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Embedded Brain Computer Interface: State-of-the-Art in Research

Belwafi

Gannouni

Aboalsamh

2021

Sensors

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“…Due to their ease, non-invasive nature, high temporal resolution, portability and low cost, most BCIs use the surface electroencephalography (EEG) as the preferred method to obtain BCI control signals ( Radaman and Vasilakos, 2017 ). To implement EEG-based BCI systems several protocols and paradigms (e.g., imagery or visual tasks) have been used to modulate the subject’s brain electrical activity ( Abiri et al, 2019 ; Bonci et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Algorithms in Visual Evoked Potential-Based Brain-Computer Interfaces for Motor Rehabilitation Applications: Systematic Review and Future Directions

Gutiérrez-Martínez

Mercado-Gutierrez

Carvajal-Gámez

et al. 2021

Front. Hum. Neurosci.

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Brain-Computer Interface (BCI) is a technology that uses electroencephalographic (EEG) signals to control external devices, such as Functional Electrical Stimulation (FES). Visual BCI paradigms based on P300 and Steady State Visually Evoked potentials (SSVEP) have shown high potential for clinical purposes. Numerous studies have been published on P300- and SSVEP-based non-invasive BCIs, but many of them present two shortcomings: (1) they are not aimed for motor rehabilitation applications, and (2) they do not report in detail the artificial intelligence (AI) methods used for classification, or their performance metrics. To address this gap, in this paper the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology was applied to prepare a systematic literature review (SLR). Papers older than 10 years, repeated or not related to a motor rehabilitation application, were excluded. Of all the studies, 51.02% referred to theoretical analysis of classification algorithms. Of the remaining, 28.48% were for spelling, 12.73% for diverse applications (control of wheelchair or home appliances), and only 7.77% were focused on motor rehabilitation. After the inclusion and exclusion criteria were applied and quality screening was performed, 34 articles were selected. Of them, 26.47% used the P300 and 55.8% the SSVEP signal. Five applications categories were established: Rehabilitation Systems (17.64%), Virtual Reality environments (23.52%), FES (17.64%), Orthosis (29.41%), and Prosthesis (11.76%). Of all the works, only four performed tests with patients. The most reported machine learning (ML) algorithms used for classification were linear discriminant analysis (LDA) (48.64%) and support vector machine (16.21%), while only one study used a deep learning algorithm: a Convolutional Neural Network (CNN). The reported accuracy ranged from 38.02 to 100%, and the Information Transfer Rate from 1.55 to 49.25 bits per minute. While LDA is still the most used AI algorithm, CNN has shown promising results, but due to their high technical implementation requirements, many researchers do not justify its implementation as worthwile. To achieve quick and accurate online BCIs for motor rehabilitation applications, future works on SSVEP-, P300-based and hybrid BCIs should focus on optimizing the visual stimulation module and the training stage of ML and DL algorithms.

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“…For example, event-related potentials (ERPs) such as P300 [ 8 ], sensorimotor rhythms (SMRs) such as

-rhythm [ 9 , 10 ], the steady-state visual evoked potential (SSVEP), and auditory or tactile evoked responses [ 11 , 12 ] have been used for BCIs. A recent review of BCI technologies and applications appeared in [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Steady-State Visual Evoked Potential Classification Using Complex Valued Convolutional Neural Networks

Ikeda

Washizawa

2021

Sensors

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The steady-state visual evoked potential (SSVEP), which is a kind of event-related potential in electroencephalograms (EEGs), has been applied to brain–computer interfaces (BCIs). SSVEP-based BCIs currently perform the best in terms of information transfer rate (ITR) among various BCI implementation methods. Canonical component analysis (CCA) or spectrum estimation, such as the Fourier transform, and their extensions have been used to extract features of SSVEPs. However, these signal extraction methods have a limitation in the available stimulation frequency; thus, the number of commands is limited. In this paper, we propose a complex valued convolutional neural network (CVCNN) to overcome the limitation of SSVEP-based BCIs. The experimental results demonstrate that the proposed method overcomes the limitation of the stimulation frequency, and it outperforms conventional SSVEP feature extraction methods.

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An Introductory Tutorial on Brain–Computer Interfaces and Their Applications

Cited by 36 publications

References 264 publications

Embedded Brain Computer Interface: State-of-the-Art in Research

Embedded Brain Computer Interface: State-of-the-Art in Research

Artificial Intelligence Algorithms in Visual Evoked Potential-Based Brain-Computer Interfaces for Motor Rehabilitation Applications: Systematic Review and Future Directions

Steady-State Visual Evoked Potential Classification Using Complex Valued Convolutional Neural Networks

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