Emotiv-Based Low-Cost Brain Computer Interfaces: A Survey

Masood, Naveen; Farooq, Humera

doi:10.1007/978-3-319-41691-5_12

Cited by 6 publications

(3 citation statements)

References 31 publications

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“…The Emotiv EPOC+ headset [41]- [52] offers a costeffective solution and has been increasingly employed in brain-computer interface (BCI) applications such as device control, gaming, emotion detection, user brain state detection, robots [41], and motor imagery applications [50]. The Emotiv EPOC headset shows a good performance when detecting mental activity with the aim of identifying several mental actions [51].…”

Section: B Experiments Procedures and Eeg Data Acquisitionmentioning

confidence: 99%

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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Section: B Experiments Procedures and Eeg Data Acquisitionmentioning

confidence: 99%

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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“…They have found the accuracy of the 32-channel Biosemi headset to be 88.5% and the Emotiv to be 61.7%. Many other studies have examined performance of commercial EEG recorders employing other visually evoked BCIs like SSVEP and obtained similar results [3], [4].…”

Section: Introductionmentioning

confidence: 53%

“…A second important aspect is the type of visual stimuli and area affected by different classes stimuli. For example, many accuracies above 95% have been reported for the P300oddball and SSVEP paradigms [5], [3], [4]. Meanwhile, face recognition based classification accuracies are rather inferior to this, just as discussed in the following.…”

Section: Introductionmentioning

confidence: 91%

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

Abdulaal

Casson

Gaydecki

2018

2018 26th European Signal Processing Conference (EUSIPCO)

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Brain-Computer Interface (BCI) is a technology that utilizes brainwaves to link the brain with external machines for either medical analysis, or to improve quality of life such as control and communication for people affected with paralysis. The performance of BCI systems depends on classification accuracy, which influences the Information Transfer Rate. This motivates researchers to improve their classification accuracy as best possible. A bias problem in reporting accuracies by using non-nested cross-validation methods was thought to increase accuracy. The aim of this paper was to validate and quantify such a concept by using a low-cost commercial EEG recorder to classify visually evoking face vs scrambled pictures, and report high accuracy using non-nested cross validation. The algorithm employed Independent Component Analysis followed by feature extraction with sample covariance matrices. The data were then classified using Support Vector Machines. The accuracy was tested with nested and non-nested cross-validation methods; accuracies obtained were 63% and 76%, respectively.

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MIDNN- a classification approach for the EEG based motor imagery tasks using deep neural network

2021

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Emotiv-Based Low-Cost Brain Computer Interfaces: A Survey

Cited by 6 publications

References 31 publications

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

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

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

MIDNN- a classification approach for the EEG based motor imagery tasks using deep neural network

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