Noise Robustness Analysis of Performance for EEG-Based Driver Fatigue Detection Using Different Entropy Feature Sets

Motor Imagery EEG Classification Based on a Weighted Multi-Branch Structure Suitable for Multisubject Data

OBJECTIVE

Electroencephalogram (EEG) signal recognition based on deep learning technology requires the support of sufficient data. However, training data scarcity usually occurs in subject-specific motor imagery tasks unless multisubject data can be used to enlarge training data. Unfortunately, because of the large discrepancies between data distributions from different subjects, model performance could only be improved marginally or even worsened by simply training on multisubject data.

METHOD

This article proposes a novel weighted multi-branch (WMB) structure for handling multisubject data to solve the problem, in which each branch is responsible for fitting a pair of source-target subject data and adaptive weights are used to integrate all branches or select branches with the largest weights to make the final decision. The proposed WMB structure was applied to six well-known deep learning models (EEGNet, Shallow ConvNet, Deep ConvNet, ResNet, MSFBCNN, and EEG_TCNet) and comprehensive experiments were conducted on EEG datasets BCICIV-2a, BCICIV-2b, high gamma dataset (HGD) and two supplementary datasets.

RESULT

Superior results against the state-of-the-art models have demonstrated the efficacy of the proposed method in subject-specific motor imagery EEG classification. For example, the proposed WMB_EEGNet achieved classification accuracies of 84.14%, 90.23%, and 97.81% on BCICIV-2a, BCICIV-2b and HGD, respectively.

CONCLUSION

It is clear that the proposed WMB structure is capable to make good use of multisubject data with large distribution discrepancies for subject-specific EEG classification.

Linear and non-linear feature extraction from rat electrocorticograms for seizure detection by support vector machine

Seizures, the main symptom of epilepsy, are provoked due to a neurological disorder that underlies the disease. The accurate detection of seizures is a crucial step in any procedure of treatment. In the present study, electrocorticogram (ECoG) signals were recorded from awake and freely moving animals implanted with cortical electrodes before and after pentylenetetrazol, the chemo-convulsant injection. ECoG signals were segmented into 4-s epochs and labeled. Twenty-four linear and non-linear features were extracted from the time and frequency domains of the ECoG signals. The extracted features either individually or in combinations were fed to an automatic support vector machine (SVM) classification system. SVM classifier was trained with 5 min of ictal and non-ictal labeled ECoG signals to build the hyperplane that separates two sets of training signals. Sensitivity, specificity, and accuracy were determined for the testing dataset using the different feature combinations. It has been found that some linear features either individually or in combinations outperform non-linear features in terms of the accuracy for seizure detection. The maximum accuracy achieved by the system was 95.3% and has been obtained only after linear and non-linear features were combined. ECoG signals were classified without pre-processing or removal of artifacts to reduce the required computational time to be suitable for online implementation purposes. This may prove the detection system's robustness and supports its use in online seizure detection protocols.

Fatigue Detection with Spatial-Temporal Fusion Method on Covariance Manifolds of Electroencephalography

With the increasing pressure of current life, fatigue caused by high-pressure work has deeply affected people and even threatened their lives. In particular, fatigue driving has become a leading cause of traffic accidents and deaths. This paper investigates electroencephalography (EEG)-based fatigue detection for driving by mining the latent information through the spatial-temporal changes in the relations between EEG channels. First, EEG data are partitioned into several segments to calculate the covariance matrices of each segment, and then we feed these matrices into a recurrent neural network to obtain high-level temporal information. Second, the covariance matrices of whole signals are leveraged to extract two kinds of spatial features, which will be fused with temporal characteristics to obtain comprehensive spatial-temporal information. Experiments on an open benchmark showed that our method achieved an excellent classification accuracy of 93.834% and performed better than several novel methods. These experimental results indicate that our method enables better reliability and feasibility in the detection of fatigued driving.

Development of single-channel electroencephalography signal analysis model for real-time drowsiness detection : SEEGDD

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.

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Drowsiness detection (DD) has become a relevant area of active research in biomedical signal processing. Recently, various deep learning (DL) researches based on the EEG signals have been proposed to detect fatigue conditions. The research presented in this paper proposes an EEG classification system for DD based on DL networks. However, the proposed DD system is mainly realized into two procedures; (i) data acquisition and (ii) model analysis. For the data acquisition procedure, two key steps are considered, which are the signal collection using a wearable Emotiv EPOC+ headset to record 14 channels of EEG, and the signal annotation. Furthermore, a data augmentation (DA) step has been added to the proposed system to overcome the problem of over-fitting and to improve accuracy. As regards the model analysis, a comparative study is also introduced in this paper to argue the choice of DL architecture and frameworks used in our DD system. In this sense, The proposed DD protocol makes use of a convolutional neural network (CNN) architecture implemented using the Keras library. The results showed a high accuracy value (90.42%) in drowsy/awake discrimination and revealed the efficiency of the proposed DD system compared to other research works.

Constructing Multi-scale Entropy Based on the Empirical Mode Decomposition(EMD) and its Application in Recognizing Driving Fatigue

BACKGROUND

Fatigue is one of the important factors in traffic accidents. Hence, it is necessary to devise methods to detect the fatigue and apply practical fatigue detection solutions for drivers.

NEW METHOD

This paper presents a method based on the empirical mode decomposition(EMD) of multi-scale entropy on the recorded forehead Electroencephalogram(EEG) signals. These EEG signals are decomposed to extract intrinsic mode functions(IMFs) by using the EMD technique. Then, the IMFs components are selected out by using the Pearson correlation coefficient and the best scale features on each signal are determined in multiple experiments.

RESULTS

Results indicate that the empirical mode decomposition multi-scale fuzzy entropy feature classification recognition rate is up to 87.50%, the highest is 88.74%, which is 23.88% higher than the single-scale fuzzy entropy and 5.56% higher than multi-scale fuzzy entropy.

COMPARISON WITH EXISTING METHOD

Three types of entropies measures, permutation entropy(PE), sample entropy(SE), fuzzy entropy(FE), were applied for the analysis of signal and compared by seven classifiers in 10-fold and Leave-One-Out cross-validation experiments.

CONCLUSIONS

The proposed method can be effectively applied to the detection of driving fatigue.

Conditional Adversarial Domain Adaptation Neural Network for Motor Imagery EEG Decoding

Decoding motor imagery (MI) electroencephalogram (EEG) signals for brain-computer interfaces (BCIs) is a challenging task because of the severe non-stationarity of perceptual decision processes. Recently, deep learning techniques have had great success in EEG decoding because of their prominent ability to learn features from raw EEG signals automatically. However, the challenge that the deep learning method faces is that the shortage of labeled EEG signals and EEGs sampled from other subjects cannot be used directly to train a convolutional neural network (ConvNet) for a target subject. To solve this problem, in this paper, we present a novel conditional domain adaptation neural network (CDAN) framework for MI EEG signal decoding. Specifically, in the CDAN, a densely connected ConvNet is firstly applied to obtain high-level discriminative features from raw EEG time series. Then, a novel conditional domain discriminator is introduced to work as an adversarial with the label classifier to learn commonly shared intra-subjects EEG features. As a result, the CDAN model trained with sufficient EEG signals from other subjects can be used to classify the signals from the target subject efficiently. Competitive experimental results on a public EEG dataset (High Gamma Dataset) against the state-of-the-art methods demonstrate the efficacy of the proposed framework in recognizing MI EEG signals, indicating its effectiveness in automatic perceptual decision decoding.

A hybrid model for EEG-based gender recognition

The gender recognition is an important research field to study evidence regarding some personal characteristics in the information and data society. However, some current traditional methods such as vision and sound have been exposed their own security weaknesses. Recently, biometric gender recognition based on Electroencephalography (EEG) signals has been widely used in information safety and medical fields. It is necessary to explore potential of using EEG to present a more robust and accurate result with larger training data based on sophisticated machine learning approaches. In this contribution, we present an automated gender recognition system by a hybrid model based on EEG data of resting state from twenty-eight subjects. These data are useful and handy to get insights into assessing the differences in personal gender. For achieving a good performance and a strong robustness, the system develops a hybrid model of combining random forest and logistic regression, and employs four common entropy measures to analyze the non-stationary EEG signals. Result also suggests that the recognition performance achieve an improved progress with an accuracy of 0.9982 and AUC of 0.9926 based on a nested tenfold cross-validation loop, implying that show a significant potential applicability of the proposed approach and is capable of recognizing personal gender.

Centered and Averaged Fuzzy Entropy to Improve Fuzzy Entropy Precision

Several entropy measures are now widely used to analyze real-world time series. Among them, we can cite approximate entropy, sample entropy and fuzzy entropy (FuzzyEn), the latter one being probably the most efficient among the three. However, FuzzyEn precision depends on the number of samples in the data under study. The longer the signal, the better it is. Nevertheless, long signals are often difficult to obtain in real applications. This is why we herein propose a new FuzzyEn that presents better precision than the standard FuzzyEn. This is performed by increasing the number of samples used in the computation of the entropy measure, without changing the length of the time series. Thus, for the comparisons of the patterns, the mean value is no longer a constraint. Moreover, translated patterns are not the only ones considered: reflected, inversed, and glide-reflected patterns are also taken into account. The new measure (so-called centered and averaged FuzzyEn) is applied to synthetic and biomedical signals. The results show that the centered and averaged FuzzyEn leads to more precise results than the standard FuzzyEn: the relative percentile range is reduced compared to the standard sample entropy and fuzzy entropy measures. The centered and averaged FuzzyEn could now be used in other applications to compare its performances to those of other already-existing entropy measures.