A novel hybrid paradigm based on steady state visually evoked potential &amp; P300 to enhance information transfer rate

Katyal, Akshay; Singla, Rajesh

doi:10.1016/j.bspc.2020.101884

Cited by 14 publications

(11 citation statements)

References 47 publications

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“…To overcome this limitation, diverse stimuli colors and flickering frequencies have been proposed for hybrid BCI’s. With these variations of the visual stimulation paradigm, a good trade-off is achieved between accuracy (92.30%) and ITR (82.38 bpm), enhancing the potential to develop P300/SSVEP-based BCIs for the control of rehabilitation devices ( Katyal and Singla, 2020 ).…”

Section: Resultsmentioning

confidence: 99%

“…The SSVEP signal has some advantages over the P300: (1) no mental task is required to induce the intended potential, (2) enables subjects to use the paradigm without requiring great mental load, and (3) it achieves higher ITR. However, the number of command choices in an SSVEP paradigm is generally represented by frequencies within the band of 5–20 Hz ( Katyal and Singla, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

“…However, if a low visual stimulation frequency (interstimulus interval) is used by the visual stimulation module, the system’s ITR may be limited ( Mainsah et al, 2015 ). To overcome this limitation, diverse stimuli colors and flickering frequencies have been proposed for hybrid BCI’s achieving a good trade-off between accuracy (92.30%) and ITR (82.38 bpm) ( Katyal and Singla, 2020 ). These approaches have the potential to enhance the development and performance of P300/SSVEP-based BCIs for the control of rehabilitation devices.…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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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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“…According to how the signals are combined, the hBCIs fall into two modes. The first mode of hBCIs combines two or more kinds of EEG signals, such as event-related desynchronization (ERD), event-related synchronization (ERS), and P300 [24,25]. Another mode of hBCIs combines EEG and other signals, such as EMG signals and ECG signals [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Recognition of Drivers’ Hard and Soft Braking Intentions Based on Hybrid Brain-Computer Interfaces

Feleke

Luo

et al. 2022

Cyborg Bionic Syst

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In this paper, we propose simultaneous and sequential hybrid brain-computer interfaces (hBCIs) that incorporate electroencephalography (EEG) and electromyography (EMG) signals to classify drivers’ hard braking, soft braking, and normal driving intentions to better assist driving for the first time. The simultaneous hBCIs adopt a feature-level fusion strategy (hBCI-FL) and classifier-level fusion strategies (hBCIs-CL). The sequential hBCIs include the hBCI-SE1, where EEG signals are prioritized to detect hard braking, and hBCI-SE2, where EMG signals are prioritized to detect hard braking. Experimental results show that the proposed hBCI-SE1 with spectral features and the one-vs-rest classification strategy performs best with an average system accuracy of 96.37% among hBCIs. This work is valuable for developing human-centric intelligent assistant driving systems to improve driving safety and driving comfort and promote the application of BCIs.

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“…On the other hand, frequency-modulated VEP (f-VEP)-based BCIs represented by steady-state visual evoked potential (SSVEP) (which have been on focused more), provide a more robust response than t-VEP-based BCIs (Bin et al, 2009a;De Neeling and Van Hulle, 2019). However, most f-VEP studies adopted the frame-based "on/off " flickering stimulation method, in which only frequencies that are the aliquot number of the refresh rate could be used for coding (Yin et al, 2013b(Yin et al, , 2015Allison et al, 2014;Combaz and Van Hulle, 2015;Wang et al, 2015;Katyal and Singla, 2020). Subsequent studies proposed frequencyapproximation approaches to overcome this problem, in which adopting sampled sinusoidal stimulation to create frequencies approximated to the ideal stimulation frequency (Wang and Jung, 2010;Xie et al, 2012;Chen et al, 2014a).…”

Section: Introductionmentioning

confidence: 99%

A novel multiple time-frequency sequential coding strategy for hybrid brain-computer interface

Zan

Ren

et al. 2022

Front. Hum. Neurosci.

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BackgroundFor brain-computer interface (BCI) communication, electroencephalography provides a preferable choice due to its high temporal resolution and portability over other neural recording techniques. However, current BCIs are unable to sufficiently use the information from time and frequency domains simultaneously. Thus, we proposed a novel hybrid time-frequency paradigm to investigate better ways of using the time and frequency information.MethodWe adopt multiple omitted stimulus potential (OSP) and steady-state motion visual evoked potential (SSMVEP) to design the hybrid paradigm. A series of pre-experiments were undertaken to study factors that would influence the feasibility of the hybrid paradigm and the interaction between multiple features. After that, a novel Multiple Time-Frequencies Sequential Coding (MTFSC) strategy was introduced and explored in experiments.ResultsOmissions with multiple short and long durations could effectively elicit time and frequency features, including the multi-OSP, ERP, and SSVEP in this hybrid paradigm. The MTFSC was feasible and efficient. The preliminary online analysis showed that the accuracy and the ITR of the nine-target stimulator over thirteen subjects were 89.04% and 36.37 bits/min.SignificanceThis study first combined the SSMVEP and multi-OSP in a hybrid paradigm to produce robust and abundant time features for coding BCI. Meanwhile, the MTFSC proved feasible and showed great potential in improving performance, such as expanding the number of BCI targets by better using time information in specific stimulated frequencies. This study holds promise for designing better BCI systems with a novel coding method.

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A novel hybrid paradigm based on steady state visually evoked potential & P300 to enhance information transfer rate

Cited by 14 publications

References 47 publications

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

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

Recognition of Drivers’ Hard and Soft Braking Intentions Based on Hybrid Brain-Computer Interfaces

A novel multiple time-frequency sequential coding strategy for hybrid brain-computer interface

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