Electroencephalogram (EEG) plays an important role in identifying brain activity and behavior. However, the recorded electrical activity always be contaminated with artifacts and then affect the ...analysis of EEG signal. Hence, it is essential to develop methods to effectively detect and extract the clean EEG data during encephalogram recordings. Several methods have been proposed to remove artifacts, but the research on artifact removal continues to be an open problem. This paper tends to review the current artifact removal of various contaminations. We first discuss the characteristics of EEG data and the types of different artifacts. Then, a general overview of the state-of-the-art methods and their detail analysis are presented. Lastly, a comparative analysis is provided for choosing a suitable methods according to particular application.
Neurofeedback System Lochotinunt, Chanin; Suwanpathumlert, Nattasasi; Masawat, Nattachai ...
2019 12th Biomedical Engineering International Conference (BMEiCON),
2019-Nov.
Conference Proceeding
Nowadays, reading is one of the essential factors in learning. However, there are problems which make reading ineffective or time-wasting on comprehending the points read. The most efficient method ...to read is associated with our concentration. Practicing concentration allows us to control this factor. In this study, neurofeedback is applied in this situation together with the Neurosky Mindset measuring Electroencephalogram (EEG) to indicate the concentration level by the lamp which connects to the Arduino board informing the user about their state of concentration from the output. The output illustrates that the light will dim when the concentration level drops below the threshold. Making lamp's light brighter, you need to adjust your concentration level to be higher than a threshold. Moreover, the concentration level output will be demonstrated on the smartphone application.
Due to severe cross-subject data variations in electroencephalogram (EEG) signals, the issue of subject-independent EEG-based emotion recognition remains challenging till today. To cope with this ...challenge, we propose a novel and effective dynamic stream selection network (DSSN), which can adaptively adjust its structure according to the characteristics of signal data from different individuals for this issue. DSSN consists of a tri-stream structure and a dynamic selection network. The tri-stream structure takes charge of extracting the spatial, the temporal, and the spatio-temporal features, respectively, for emotion classification. The dynamic selection network is responsible for selecting the most suitable stream for every subject. Subject-independent experiments on the benchmarks DEAP, DREAMER, and SEED-IV have readily demonstrated the advantage of DSSN over the related advanced approaches.
•CNN and LSTM are integrated for efficient detection of the epileptic seizure.•A comparison among time, frequency and time–frequency features are presented.•A highest classification accuracy of ...99.27% is achieved.•The proposed method is useful for binary and multi-class classification problems.
Advances in deep learning methods present new opportunities for fixing complex problems for an end to end learning. In terms of optimal design, seizure detection from EEG data has not been completely exploited by current models of deep learning. Most of the previous studies focus on handcrafted feature extraction for seizure detection. However, this method is not generalizable and needs major changes inside a new dataset for each new patient. In this paper, we proposed autonomously generalized retrospective and patient-specific hybrid models based on two types of feature extractors, namely Convolutional Neural Networks along with long short-term memory. The model automatically generates customized features to better classify ictal, interictal, and preictal segments for each patient and make it ideal for real-time. The procedure can be extended to any patient from Freiburg epileptic seizure database without the need for manual feature extraction. The method decomposed the EEG signals into time-based, frequency-based, and time–frequency-based features that were tested and compared in 21 subjects. Three forms of experiments including two binary classification problems and a ternary classification were performed to investigate the feasibility of the proposed approach. Using the time–frequency domain signals an average accuracy of 99.19%, 99.27%, and 95.04%, with frequency-domain signals, average accuracies of 96.64%, 95.75%, and 93.42% while with time-domain signals an average accuracy of 94.71%, 93.99%, and 90.53% was obtained. Our work shows that the combined use of CNNs and LSTMs by integrating spatial and temporal context along with time–frequency domain signals can significantly improve the accuracy of seizure detection.
Driver fatigue evaluation is of great importance for traffic safety and many intricate factors would exacerbate the difficulty. In this paper, based on the spatial-temporal structure of multichannel ...electroencephalogram (EEG) signals, we develop a novel EEG-based spatial-temporal convolutional neural network (ESTCNN) to detect driver fatigue. First, we introduce the core block to extract temporal dependencies from EEG signals. Then, we employ dense layers to fuse spatial features and realize classification. The developed network could automatically learn valid features from EEG signals, which outperforms the classical two-step machine learning algorithms. Importantly, we carry out fatigue driving experiments to collect EEG signals from eight subjects being alert and fatigue states. Using 2800 samples under within-subject splitting, we compare the effectiveness of ESTCNN with eight competitive methods. The results indicate that ESTCNN fulfills a better classification accuracy of 97.37% than these compared methods. Furthermore, the spatial-temporal structure of this framework advantages in computational efficiency and reference time, which allows further implementations in the brain-computer interface online systems.
•Proposed P-1D-CNN model for detecting epilepsy that has far less learnable parameters.•To deal with the small amount of available data, proposed two augmentation schemes.•Proposed an epilepsy ...detection system as an ensemble of P-1D-CNN models.•Thoroughly evaluated the augmentation schemes and the deep models.•The system gives an accuracy of 99.1 ± 0.9% on the University of Bonn dataset.
Epilepsy is a life-threatening and challenging neurological disorder, which is affecting a large number of people all over the world. For its detection, encephalography (EEG) is a commonly used clinical approach, but manual inspection of EEG brain signals is a time-consuming and laborious process, which puts a heavy burden on neurologists and affects their performance. Several automatic systems have been proposed using traditional approaches to assist neurologists, which perform well in detecting binary epilepsy scenarios e.g. normal vs. ictal, but their performance degrades in classifying ternary case e.g. ictal vs. normal vs. inter-ictal. To overcome this problem, we propose a system that is an ensemble of pyramidal one-dimensional convolutional neural network (P-1D-CNN) models. Though a CNN model learns the internal structure of data and outperforms hand-engineered techniques, the main issue is the large number of learnable parameters, whose learning requires a huge volume of data. To overcome this issue, P-1D-CNN works on the concept of refinement approach and it involves 61% fewer parameters compared to standard CNN models and as such it has better generalization. Further to overcome the limitations of the small amount of data, we propose two augmentation schemes. We tested the system on the University of Bonn dataset, a benchmark dataset; in almost all the cases concerning epilepsy detection, it gives an accuracy of 99.1 ± 0.9% and outperforms the state-of-the-art systems. In addition, while enjoying the strength of a CNN model, P-1D-CNN model requires 61% less memory space and its detection time is very short (< 0.000481 s), as such it is suitable for real-time clinical setting. It will ease the burden of neurologists and will assist the patients in alerting them before the seizure occurs. The proposed P-1D-CNN model is not only suitable for epilepsy detection, but it can be adopted in developing robust expert systems for other similar disorders.
Objective: We propose a novel complexity measure to overcome the deficiencies of the widespread and powerful multiscale entropy (MSE), including, MSE values may be undefined for short signals, and ...MSE is slow for real-time applications. Methods: We introduce multiscale dispersion entropy (DisEn-MDE) as a very fast and powerful method to quantify the complexity of signals. MDE is based on our recently developed DisEn, which has a computation cost of O(N), compared with O(N 2 ) for sample entropy used in MSE. We also propose the refined composite MDE (RCMDE) to improve the stability of MDE. Results: We evaluate MDE, RCMDE, and refined composite MSE (RCMSE) on synthetic signals and three biomedical datasets. The MDE, RCMDE, and RCMSE methods show similar results, although the MDE and RCMDE are faster, lead to more stable results, and discriminate different types of physiological signals better than MSE and RCMSE. Conclusion: For noisy short and long time series, MDE and RCMDE are noticeably more stable than MSE and RCMSE, respectively. For short signals, MDE and RCMDE, unlike MSE and RCMSE, do not lead to undefined values. The proposed MDE and RCMDE are significantly faster than MSE and RCMSE, especially for long signals, and lead to larger differences between physiological conditions known to alter the complexity of the physiological recordings. Significance : MDE and RCMDE are expected to be useful for the analysis of physiological signals thanks to their ability to distinguish different types of dynamics. The MATLAB codes used in this paper are freely available at http://dx.doi.org/10.7488/ds/1982.
The electroencephalogram (EEG) is the most promising and efficient technique to study epilepsy and record all the electrical activity going in our brain. Automated screening of epilepsy through ...data-driven algorithms reduces the manual workload of doctors to diagnose epilepsy. New algorithms are biased either towards signal processing or deep learning, which holds subjective advantages and disadvantages. The proposed pipeline is an end-to-end automated seizure prediction framework with a Fourier transform feature extraction and deep learning-based transformer model, a blend of signal processing and deep learning - this imbibes the potential features to automatically identify the attentive regions in EEG signals for effective screening. The proposed pipeline has demonstrated superior performance on the benchmark dataset with average sensitivity and false-positive rate per hour (FPR/h) as 98.46%, 94.83% and 0.12439, 0, respectively. The proposed work shows great results on the benchmark datasets and a big potential for clinics as a support system with medical experts monitoring the patients.
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