Driver drowsiness detection methods using EEG signals: a systematic review

Hussein, Raed Mohammed; Miften, Firas Sabar; George, Loay E.

doi:10.1080/10255842.2022.2112574

Cited by 11 publications

(4 citation statements)

References 53 publications

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“…This study also excluded delta and gamma waves because they were not relevant to the research on drowsiness detection. In particular, delta waves frequently occur during deep sleep state [ 47 , 48 ], while gamma waves are related to excitement feeling and higher brain functions, such as learning, memory, and information processing [ 33 , 49 ].…”

Section: Methodsmentioning

confidence: 99%

“…In recent years, several research works have focused on drowsiness detection while driving using EEG signal as an indicator. However, none of these studies have used the combinations of power-α, power-β, power-θ and four ratios of brainwaves (θ/β, θ/[α + β], [θ + α]/β, and [θ + α]/[α + β]) [ 33 ]. In addition, there is a need for detecting fatigue and drowsiness in real time while driving [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of drowsiness using EEG signals in young Indonesian drivers

Puspasari,

Syaifullah,

Iqbal

et al. 2023

Heliyon

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of drowsiness using EEG signals in young Indonesian drivers

Puspasari,

Syaifullah,

Iqbal

et al. 2023

Heliyon

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“…proposed a new method for driver drowsiness detection by analyzing EEG signals in detail and utilizing complex networks and deep learning to address the related issues in driver drowsiness detection. It provided data support for reducing driver damage caused by fatigue [16].…”

Section: Related Workmentioning

confidence: 99%

Analysis of EEG Signals Fused With CNN and Complex Networks

Ma,

2024

IEEE Access

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Currently, brain-computer interface technology still poses hidden dangers in the complete control of ideas. Additionally, there are issues with the low sampling frequency and accuracy of EEG signal acquisition equipment. To address these concerns, this study proposes a combined model for EEG signal recognition and classification analysis by combining frequency division multi-feature complex brain networks with parallel convolutional neural networks. The effectiveness of this model has been verified. In model visualization analysis, the visualization results of t-distribution random neighborhood embedding in the third separable convolutional layer indicate that the two types of imagination have already experienced separation. There is a clear boundary between the two at position 0 on both the horizontal and vertical axes. This is a significant improvement compared to the comparative model. In the model performance verification, the full band classification accuracy in the synchronous network was maintained between 60% to 84%, and the μ-rhythm was maintained between 59% to 81%. The average classification accuracy of the combined model was 77.40% with higher performance, which was higher than 68.53% and 70.87% of the single scale convolutional neural network. In comparison with deep learning algorithms, the average classification accuracy of the combined model was 85.74%, much higher than the 66.20% and 76.69% of the comparative models. The composite model constructed has good performance in recognizing and classifying electroencephalogram signals. It can be effectively applied in practical brain-computer interface technology or electroencephalogram signal analysis.

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“…The recent progress of DL has significantly increased its relevance for EEG data analysis (Roy et al, 2019). Domains of application include emotion recognition (Houssein et al, 2022), driver drowsiness (Stancin et al, 2021;Mohammed et al, 2022), classification of alcoholic EEG (Farsi et al, 2021), epileptic seizure detection (Ahmad et al, 2022), mental disorders (de Bardeci et al, 2021), schizophrenia (Oh et al, 2019), major depressive disorder and bipolar disorder detection (Yasin et al, 2021), motor imagery and other brain computer interface (BCI)-related problems (Lotte et al, 2018;Abo Alzahab et al, 2021). Despite the attention of DL in EEG, little research has focused on issues relating to the crossdataset setting and generalization (Wei et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Introducing Region Based Pooling for handling a varied number of EEG channels for deep learning models

Tveitstøl,

Tveter,

Pérez T.

et al. 2024

Front. Neuroinform.

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IntroductionA challenge when applying an artificial intelligence (AI) deep learning (DL) approach to novel electroencephalography (EEG) data, is the DL architecture's lack of adaptability to changing numbers of EEG channels. That is, the number of channels cannot vary neither in the training data, nor upon deployment. Such highly specific hardware constraints put major limitations on the clinical usability and scalability of the DL models.MethodsIn this work, we propose a technique for handling such varied numbers of EEG channels by splitting the EEG montages into distinct regions and merge the channels within the same region to a region representation. The solution is termed Region Based Pooling (RBP). The procedure of splitting the montage into regions is performed repeatedly with different region configurations, to minimize potential loss of information. As RBP maps a varied number of EEG channels to a fixed number of region representations, both current and future DL architectures may apply RBP with ease. To demonstrate and evaluate the adequacy of RBP to handle a varied number of EEG channels, sex classification based solely on EEG was used as a test example. The DL models were trained on 129 channels, and tested on 32, 65, and 129-channels versions of the data using the same channel positions scheme. The baselines for comparison were zero-filling the missing channels and applying spherical spline interpolation. The performances were estimated using 5-fold cross validation.ResultsFor the 32-channel system version, the mean AUC values across the folds were: RBP (93.34%), spherical spline interpolation (93.36%), and zero-filling (76.82%). Similarly, on the 65-channel system version, the performances were: RBP (93.66%), spherical spline interpolation (93.50%), and zero-filling (85.58%). Finally, the 129-channel system version produced the following results: RBP (94.68%), spherical spline interpolation (93.86%), and zero-filling (91.92%).ConclusionIn conclusion, RBP obtained similar results to spherical spline interpolation, and superior results to zero-filling. We encourage further research and development of DL models in the cross-dataset setting, including the use of methods such as RBP and spherical spline interpolation to handle a varied number of EEG channels.

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Driver drowsiness detection methods using EEG signals: a systematic review

Cited by 11 publications

References 53 publications

Prediction of drowsiness using EEG signals in young Indonesian drivers

Prediction of drowsiness using EEG signals in young Indonesian drivers

Analysis of EEG Signals Fused With CNN and Complex Networks

Introducing Region Based Pooling for handling a varied number of EEG channels for deep learning models

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