Systematic Review of Single-Channel EEG-Based Drowsiness Detection Methods Venkata Phanikrishna Balam IEEE Transactions on Intelligent Transportation Systems, 2024 Drowsiness is characterized by reduced attentiveness, commonly experienced during the transition from wakefulness to sleepiness. It can decrease an individual’s alertness, thereby increasing the risk of accidents during activities such as driving, crane operation, working in mining areas, and industrial machinery operation. The detection of drowsiness plays an important role in preventing such accidents. Numerous methods exist for drowsiness detection, including Subjective, Vehicle, Behavioral, and Physiological approaches. Among these, Physiological methods, particularly those utilizing Electroencephalogram (EEG) data combined with artificial intelligence, have proven effective in detecting drowsiness. These methods excel in capturing physiological changes in the body during drowsiness and the potential for gathering information from the human brain during this state. EEG data-based Brain-Computer interface (BCI) systems have been popular for detecting drowsiness. Single-channel EEG signal analysis BCIs have been highly preferred for their ease and convenient usage in real-time applications. While some progress has been made in the single-channel EEG BCI, substantial progress is still needed. This paper provides a state-of-the-art analysis of recent developments in the single-channel EEG-based drowsiness detection methods. Ultimately, this review study explores potential avenues for the future development of single-channel EEG-based drowsiness detection.
Development of single-channel electroencephalography signal analysis model for real-time drowsiness detection: SEEGDD Venkata Phanikrishna Balam, Suchismitha Chinara Physical and Engineering Sciences in Medicine, 2021 Drowsiness detection is essential in some critical tasks such as vehicle driving, crane operating, mining blasting, and so on, which can help minimize the risks of inattentiveness. Electroencephalography (EEG) based drowsiness detection methods have been shown to be effective. However, due to the non-stationary nature of EEG signals, techniques such as signal transformation and sub-band extraction are increasingly being used to automatically classify awake and drowsy states. Most of these procedures require high computation time. In this paper, analytical and single-feature computation are used to propose a single-channel EEG-based drowsiness detection method to overcome this. Physionet sleep dataset and the simulated virtual driving dataset were used to test the proposed model. When compared to existing work, the proposed approach yields better results.
Automatic classification methods for detecting drowsiness using wavelet packet transform extracted time-domain features from single-channel EEG signal Venkata Phanikrishna B, Suchismitha Chinara Journal of Neuroscience Methods, 2021 BACKGROUND Detecting human drowsiness during some critical works like vehicle driving, crane operating, mining blasting, etc. is one of the safeguards to prevent accidents. Among several drowsiness detection (DD) methods, a combination of neuroscience and computer science knowledge has a better ability to differentiate awake and sleep states. Most of the current models are implemented using multi-sensors electroencephalogram (EEG) signals, multi-domain features, predefined features selection algorithms. Therefore, there is great interest in the method of detecting drowsiness on embedded platforms with improved accuracy using generalized best features. New-Method: Single-channel EEG based drowsiness detection (DD) model is proposed in this by utilizing wavelet packet transform (WPT) to extract the time-domain features from considered channel EEG. The dimension of the feature vector is reduced by the proposed novel feature selection method. RESULTS The proposed model on freely available real-time sleep analysis EEG and Simulated Virtual Driving Driver (SVDD) EEG achieves 94.45% and 85.3% accuracy, respectively. Comparison-with-Existing-Method: The results show that the proposed DD method produces better accuracy compared to the state-of-the-art using the physiological dataset with the proposed time-domain sub-band-based features and feature selection method. This task of detecting drowsiness by analyzing the 5-seconds EEG signal with four features is an improvement to my previous work on detecting drowsiness using a 30-seconds EEG signal with 66 features. CONCLUSIONS Time-domain features obtained from EEG time-domain sub-bands collected using WPT achieving excellent accuracy rate by selecting unique optimization features for all subjects by the proposed feature selection algorithm.
Statistical Channel Selection Method for Detecting Drowsiness through Single-Channel EEG-Based BCI System Venkata Phanikrishna Balam, Suchismitha Chinara IEEE Transactions on Instrumentation and Measurement, 2021 Electroencephalogram (EEG) is an essential tool used to analyze the activities effectively and different states of the brain. Drowsiness is a short period state of the brain that is also called an inattentiveness state. Drowsiness can be observed during the transition from being awake state to a sleepy state. Drowsiness can reduce a person’s alertness that increases accidental risks when involved in their personal and professional activities like vehicle driving, operating a crane, mine blasts, and so on. Drowsiness detection (DD) has a significant role in preventing accidents. Neuroscience with artificial intelligence algorithms used to detect drowsiness is also popularly known as brain–computer interface (BCI) systems. Single-channel EEG BCIs are highly preferred for convenient use in real-time applications, even though there are many challenges in the actual experimental process. They are choosing the best single-channel and classifier. In this article, a novel channel selection approach is proposed for a single-channel EEG-BCI system by integrating the statistical characteristics of the available channel’s EEG signal. In addition to this, a deep neural network (DNN) classifier is developed using the stack ensemble process for better classification accuracy. Simulated-virtual-driving driver and physionet sleep analysis EEG datasets (PSAEDs) are used to test the proposed model. Subject-wise, cross-subject-wise, and combined subject-wise validations are also employed to improve the generalization capability of the proposed techniques in this article.
Time Domain Parameters as a feature for single-channel EEG-based drowsiness detection method Venkata Phanikrishna B, Suchismitha chinara 2020 IEEE International Students Conference on Electrical Electronics and Computer Science Sceecs 2020, 2020 Progress in the automobile industry has made life easier for us, and traffic accidents have steadily increased. A large number of vehicle accidents are caused by driver drowsiness while driving. As with many drowsiness detection methods, EEG-based methodology is considered an immediate, efficient, and promising modality. Several feature types have been used in EEG-based drowsiness detection. In this study, we presented a novel feature extraction strategy based on a single Hjorth parameter, and compare its classification capability with the existing Power spectral density (PSD) feature. The results show that the proposed H-parameter features have higher and stronger performance compared to the PSD features of the present work. This field outperforms traditional feature extraction strategies. This is the first study, to the best of our knowledge, to practically apply Hjorth parameters to EEG and its sub-bands for EEG-based driver drowsiness detection.
RECENT SCHOLAR PUBLICATIONS
Advancements in electrocardiography-based detection of obstructive sleep apnea: a deep learning approach VP Balam Cutting-edge Computational Intelligence in Healthcare with Convolution and … , 2026 2026
Automated EEG signal processing: A comprehensive investigation into preprocessing techniques and sub-band extraction for enhanced brain-computer interface applications VP Balam Journal of Neuroscience Methods, 110561 , 2025 2025 Citations: 5
Systematic Review of Single-Channel EEG-Based Drowsiness Detection Methods VP Balam IEEE Transactions on Intelligent Transportation Systems 25 (11), 15210 - 15228 , 2024 2024 Citations: 17
Improved TLBO and JAYA algorithms to solve new fuzzy flexible job-shop scheduling problems R Buddala, SS Mahapatra, MR Singh, BC Balusa, P Singamsetty, ... Journal of Industrial Engineering International 18 (4), 102-114 , 2023 2023 Citations: 3
Deep Review of Machine Learning Techniques on Detection of Drowsiness Using EEG Signal B Venkata Phanikrishna, A Jaya Prakash, C Suchismitha IETE Journal of Research 69 (6), 3104-3119 , 2023 2023 Citations: 30
Analysis of EEG Signal for Drowsy Detection: A Machine Learning Approach B Venkata Phanikrishna, S Chinara Soft Computing in Interdisciplinary Sciences 988, 147-164 , 2022 2022 Citations: 6
A brief review on EEG signal pre-processing techniques for real-time brain-computer interface applications BV Phanikrishna Authorea Preprints , 2021 2021 Citations: 33
Development of single-channel electroencephalography signal analysis model for real-time drowsiness detection: SEEGDD VP Balam, S Chinara Physical and Engineering Sciences in Medicine 44 (3), 713-726 , 2021 2021 Citations: 14
Statistical channel selection method for detecting drowsiness through single-channel EEG-based BCI system VP Balam, S Chinara IEEE Transactions on Instrumentation and Measurement 70, 1-9 , 2021 2021 Citations: 41
Automated classification system for drowsiness detection using convolutional neural network and electroencephalogram VP Balam, VU Sameer, S Chinara IET Intelligent Transport Systems 15 (4), 514–524 , 2021 2021 Citations: 71
Automatic classification methods for detecting drowsiness using wavelet packet transform extracted time-domain features from single-channel EEG signal V Phanikrishna B, S Chinara Journal of neuroscience methods 347, 108927 , 2021 2021 Citations: 128
Time Domain Parameters as a feature for single-channel EEG-based drowsiness detection method V Phanikrishna B, S chinara 2020 IEEE International Students’ Conference on Electrical,Electronics and … , 2020 2020 Citations: 27
Drowsiness detection by analysis of EEG signal with the help of Machine Learning V Phanikrishna B, S Chinara, M Sarkar 24th annual International Conference on Advanced Computing and … , 2018 2018 Citations: 5
A Novel Technique for Creative Problem-Solving by using Q-learning and Association algorithm VP Balam, T Srinivasarao IJRCCT 3 (8), 809-814 , 2014 2014
A Novel Framework for Privacy Conserving Data Publishing and Handling High Dimensional Data BV PHANIKRISHNA, KSRAM PRASAD Two day National Conference on Advanced Trends and Challenges in Computer … , 2014 2014
MOST CITED SCHOLAR PUBLICATIONS
Automatic classification methods for detecting drowsiness using wavelet packet transform extracted time-domain features from single-channel EEG signal V Phanikrishna B, S Chinara Journal of neuroscience methods 347, 108927 , 2021 2021 Citations: 128
Automated classification system for drowsiness detection using convolutional neural network and electroencephalogram VP Balam, VU Sameer, S Chinara IET Intelligent Transport Systems 15 (4), 514–524 , 2021 2021 Citations: 71
Statistical channel selection method for detecting drowsiness through single-channel EEG-based BCI system VP Balam, S Chinara IEEE Transactions on Instrumentation and Measurement 70, 1-9 , 2021 2021 Citations: 41
A brief review on EEG signal pre-processing techniques for real-time brain-computer interface applications BV Phanikrishna Authorea Preprints , 2021 2021 Citations: 33
Deep Review of Machine Learning Techniques on Detection of Drowsiness Using EEG Signal B Venkata Phanikrishna, A Jaya Prakash, C Suchismitha IETE Journal of Research 69 (6), 3104-3119 , 2023 2023 Citations: 30
Time Domain Parameters as a feature for single-channel EEG-based drowsiness detection method V Phanikrishna B, S chinara 2020 IEEE International Students’ Conference on Electrical,Electronics and … , 2020 2020 Citations: 27
Systematic Review of Single-Channel EEG-Based Drowsiness Detection Methods VP Balam IEEE Transactions on Intelligent Transportation Systems 25 (11), 15210 - 15228 , 2024 2024 Citations: 17
Development of single-channel electroencephalography signal analysis model for real-time drowsiness detection: SEEGDD VP Balam, S Chinara Physical and Engineering Sciences in Medicine 44 (3), 713-726 , 2021 2021 Citations: 14
Analysis of EEG Signal for Drowsy Detection: A Machine Learning Approach B Venkata Phanikrishna, S Chinara Soft Computing in Interdisciplinary Sciences 988, 147-164 , 2022 2022 Citations: 6
Automated EEG signal processing: A comprehensive investigation into preprocessing techniques and sub-band extraction for enhanced brain-computer interface applications VP Balam Journal of Neuroscience Methods, 110561 , 2025 2025 Citations: 5
Drowsiness detection by analysis of EEG signal with the help of Machine Learning V Phanikrishna B, S Chinara, M Sarkar 24th annual International Conference on Advanced Computing and … , 2018 2018 Citations: 5
Improved TLBO and JAYA algorithms to solve new fuzzy flexible job-shop scheduling problems R Buddala, SS Mahapatra, MR Singh, BC Balusa, P Singamsetty, ... Journal of Industrial Engineering International 18 (4), 102-114 , 2023 2023 Citations: 3
Advancements in electrocardiography-based detection of obstructive sleep apnea: a deep learning approach VP Balam Cutting-edge Computational Intelligence in Healthcare with Convolution and … , 2026 2026
A Novel Technique for Creative Problem-Solving by using Q-learning and Association algorithm VP Balam, T Srinivasarao IJRCCT 3 (8), 809-814 , 2014 2014
A Novel Framework for Privacy Conserving Data Publishing and Handling High Dimensional Data BV PHANIKRISHNA, KSRAM PRASAD Two day National Conference on Advanced Trends and Challenges in Computer … , 2014 2014
