Real Time Fatigue-Driver Detection from Electroencephalography Using Emotiv EPOC+

Sarno, Riyanarto; Nugraha, Brilian T.; Munawar, Muhammad Nadzeri

doi:10.15866/irecos.v11i3.8562

Cited by 12 publications

(7 citation statements)

References 12 publications

(16 reference statements)

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“…Compared with the other devices, EEG was found to be the most costeffective means of driver detection. Nugraha et al (2016) and Sarno et al (2016) used an Emotiv headset for drowsiness detection. Data from 30 volunteers were collected during driving simulator sessions that ranged from 33 to 60 min in length.…”

Section: Openbci Ultracortexmentioning

confidence: 99%

A Systemic Review of Available Low-Cost EEG Headsets Used for Drowsiness Detection

LaRocco

Paeng

2020

Front. Neuroinform.

114

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Drowsiness is a leading cause of traffic and industrial accidents, costing lives and productivity. Electroencephalography (EEG) signals can reflect awareness and attentiveness, and low-cost consumer EEG headsets are available on the market. The use of these devices as drowsiness detectors could increase the accessibility of safety and productivity-enhancing devices for small businesses and developing countries. We conducted a systemic review of currently available, low-cost, consumer EEG-based drowsiness detection systems. We sought to determine whether consumer EEG headsets could be reliably utilized as rudimentary drowsiness detection systems. We included documented cases describing successful drowsiness detection using consumer EEG-based devices, including the Neurosky MindWave, InteraXon Muse, Emotiv Epoc, Emotiv Insight, and OpenBCI. Of 46 relevant studies, ∼27 reported an accuracy score. The lowest of these was the Neurosky Mindwave, with a minimum of 31%. The second lowest accuracy reported was 79.4% with an OpenBCI study. In many cases, algorithmic optimization remains necessary. Different methods for accuracy calculation, system calibration, and different definitions of drowsiness made direct comparisons problematic. However, even basic features, such as the power spectra of EEG bands, were able to consistently detect drowsiness. Each specific device has its own capabilities, tradeoffs, and limitations. Widely used spectral features can achieve successful drowsiness detection, even with low-cost consumer devices; however, reliability issues must still be addressed in an occupational context.

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Section: Openbci Ultracortexmentioning

confidence: 99%

A Systemic Review of Available Low-Cost EEG Headsets Used for Drowsiness Detection

LaRocco

Paeng

2020

Front. Neuroinform.

114

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“…For selecting the best accuracy rate of the proposed method, we propose to compare different results recorded by different numbers of electrodes. In [22,23], the authors discover that the prefrontal and occipital cortex are the most important channels to better diagnose the hypo-vigilance state. In this regard, we choose the following recorded data:…”

Section: Experimental Evaluationmentioning

confidence: 99%

EEG-Based Hypo-vigilance Detection Using Convolutional Neural Network

Boudaya

Bouaziz

Chaabene

et al. 2020

Lecture Notes in Computer Science

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Hypo-vigilance detection is becoming an important active research areas in the biomedical signal processing field. For this purpose, electroencephalogram (EEG) is one of the most common modalities in drowsiness and awakeness detection. In this context, we propose a new EEG classification method for detecting fatigue state. Our method makes use of a and awakeness detection. In this context, we propose a new EEG classification method for detecting fatigue state. Our method makes use of a Convolutional Neural Network (CNN) architecture. We define an experimental protocol using the Emotiv EPOC+ headset. After that, we evaluate our proposed method on a recorded and annotated dataset. The reported results demonstrate high detection accuracy (93%) and indicate that the proposed method is an efficient alternative for hypo-vigilance detection as compared with other methods.

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“…Several works have reported performance improvement with a proper signal quality. [30][31][32][33][34][35] (a) (b) Fig. 4.…”

Section: Audio Signal Reconstructionmentioning

confidence: 99%

“…The purpose of performing FFT on the signal is to change the signal from the time domain to the frequency domain so that we can know the useful information contained in the signal's frequency domain. 30 Equation 3is used to obtain the maximum frequency and the maximum frequency index, which are used to find the frequency range corresponding to the table with wavelet decomposition levels. The table with wavelet decomposition levels is obtained by the following rule 36 :…”

Section: Audio Signal Reconstructionmentioning

confidence: 99%

Classification of Music Mood Using MPEG-7 Audio Features and SVM with Confidence Interval

Sarno

Ridoean

Sunaryono

et al. 2018

Int. J. Artif. Intell. Tools

Self Cite

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Psychologically, music can affect human mood and influence human behavior. In this paper, a novel method for music mood classification is introduced. In the experiment, music mood classification was performed using feature extraction based on MPEG-7 features from the ISO/IEC 15938 standard for describing multimedia content. The result of this feature extraction are 17 low-level descriptors. Here, we used the Audio Power, Audio Harmonicity, and Audio Spectrum Projection features. Moreover, the discrete wavelet transform (DWT) was utilized for audio signal reconstruction. The reconstructed audio signals were classified by the new method, which uses a support vector machine with a confidence interval (SVM-CI). According to the experimental results, the success rate of the proposed method was satisfactory and SVM-CI outperformed the ordinary SVM.

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Real Time Fatigue-Driver Detection from Electroencephalography Using Emotiv EPOC+

Cited by 12 publications

References 12 publications

A Systemic Review of Available Low-Cost EEG Headsets Used for Drowsiness Detection

A Systemic Review of Available Low-Cost EEG Headsets Used for Drowsiness Detection

EEG-Based Hypo-vigilance Detection Using Convolutional Neural Network

Classification of Music Mood Using MPEG-7 Audio Features and SVM with Confidence Interval

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