Performance of Nested vs. Non-Nested SVM Cross-Validation Methods in Visual BCI: Validation Study

Abdulaal, Mohammed J.; Casson, Alexander J.; Gaydecki, Patrick

doi:10.23919/eusipco.2018.8553102

Cited by 9 publications

(1 citation statement)

References 13 publications

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“…This data is relevant as an indicator of the robustness and confidence on the reported performance (accuracy) of the of the AI-based classification algorithms, and of their generalization ability. When the validation methods are not explicitly reported, the certainty about the results may be questionable ( Abdulaal et al, 2018 ).…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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10 years of EPOC: A scoping review of Emotiv’s portable EEG device

Williams

McArthur

Badcock

2020

Preprint

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BACKGROUND Commercially-made low-cost electroencephalography (EEG) devices have become increasingly available over the last decade. One of these devices, Emotiv EPOC, is currently used in a wide variety of settings, including brain-computer interface (BCI) and cognitive neuroscience research. PURPOSE The aim of this study was to chart peer-reviewed reports of Emotiv EPOC projects to provide an informed summary on the use of this device for scientific purposes. METHODS We followed a five-stage methodological framework for a scoping review that included a systematic search using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. We searched the following electronic databases: PsychINFO, MEDLINE, Embase, Web of Science, and IEEE Xplore. We charted study data according to application (BCI, clinical, signal processing, experimental research, and validation) and location of use (as indexed by the first author’s address). RESULTS We identified 382 relevant studies. The top five publishing countries were the United States (n = 35), India (n = 25), China (n = 20), Poland (n = 17), and Pakistan (n = 17). The top five publishing cities were Islamabad (n = 11), Singapore (n = 10), Cairo, Sydney, and Bandung (n = 7 each). Most of these studies used Emotiv EPOC for BCI purposes (n = 277), followed by experimental research (n = 51). Thirty-one studies were aimed at validating EPOC as an EEG device and a handful of studies used EPOC for improving EEG signal processing (n = 12) or for clinical purposes (n = 11). CONCLUSIONS In its first 10 years, Emotiv EPOC has been used around the world in diverse applications, from control of robotic limbs and wheelchairs to user authentication in security systems to identification of emotional states. Given the widespread use and breadth of applications, it is clear that researchers are embracing this technology.

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The Effectiveness Assessment of Massage Therapy Using Entropy-Based EEG Features Among Lumbar Disc Herniation Patients Comparing With Healthy Controls

Fan

et al. 2020

IEEE Access

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Massage therapy (MT) is a useful complementary and alternative therapy widely used in treating low back pain (LBP), including lumbar disc herniation (LDH). However, few studies revealed the quantitative entropy-based features of electroencephalography (EEG) for the MT effectiveness for the LDH patients. This study investigated the immediate effects of Chinese massage on four EEG rhythms, using the eight entropy-based features (approximation entropy (ApEn), Sample Entropy (SampEn), wavelet entropy (WaveEn), Hilbert-Huang Transform Marginal spectrum entropy (HHTMSEn), normalized energy, permutation entropy (PE), Fuzzy entropy (FuzzyEn), and Inherent fuzzy entropy (IFE)) in the 26 LDH patients and 24 healthy controls. Results showed that after MT in LDH group, ApEn, SampEn, WaveEn, PE, FuzzyEn, and IFE features of the delta rhythm decreased, the normalized energy of delta rhythm significantly increased in the left hemisphere and theta/alpha rhythms significantly decreased. Furthermore, HHTMSEn feature of theta and beta rhythms showed the significant difference for two groups (LDH and control group) in two states (before and after massage). Ten classifiers were applied in classifying two groups or two states in one group, and most of them reached high averaged accuracies and the area under the curve (AUC), even with different length per epoch (1024, 512, and 256 points). It indicated that the entropy-based features and permutation disalignment index (PDI) of EEG rhythms could be promising indices of the massage effectiveness for LDH patients and control group. Rhythms' SampEn scalp topography in two groups showed the significant different complexity change between two states (before and after massage). INDEX TERMS Massage therapy (MT), electroencephalograpy (EEG), Lumbar disc herniation (LDH), sample entropy (SampEn), Hilbert-Huang transform marginal spectrum entropy (HHTMSEn), wavelet entropy (WaveEn), permutation entropy (PE), fuzzy entropy (FuzzyEn), inherent fuzzy entropy (IFE).

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Performance of Nested vs. Non-Nested SVM Cross-Validation Methods in Visual BCI: Validation Study

Cited by 9 publications

References 13 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

10 years of EPOC: A scoping review of Emotiv’s portable EEG device

The Effectiveness Assessment of Massage Therapy Using Entropy-Based EEG Features Among Lumbar Disc Herniation Patients Comparing With Healthy Controls

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