EEG-Based User Reaction Time Estimation Using Riemannian Geometry Features

Riemannian geometry has been successfully used in many brain-computer interface (BCI) classification problems and demonstrated superior performance. In this paper, for the first time, it is applied to BCI regression problems, an important category of BCI applications. More specifically, we propose a new feature extraction approach for Electroencephalogram (EEG) based BCI regression problems: a spatial filter is first used to increase the signal quality of the EEG trials and also to reduce the dimensionality of the covariance matrices, and then Riemannian tangent space features are extracted. We validate the performance of the proposed approach in reaction time estimation from EEG signals measured in a large-scale sustained-attention psychomotor vigilance task, and show that compared with the traditional powerband features, the tangent space features can reduce the root mean square estimation error by 4.30-8.30%, and increase the estimation correlation coefficient by 6.59-11.13%.Comment: arXiv admin note: text overlap with arXiv:1702.0291

Single-channel EEG classification of sleep stages based on REM microstructure

Rapid-eye movement (REM) sleep, or paradoxical sleep, accounts for 20–25% of total night-time sleep in healthy adults and may be related, in pathological cases, to parasomnias. A large percentage of Parkinson's disease patients suffer from sleep disorders, including REM sleep behaviour disorder and hypokinesia; monitoring their sleep cycle and related activities would help to improve their quality of life. There is a need to accurately classify REM and the other stages of sleep in order to properly identify and monitor parasomnias. This study proposes a method for the identification of REM sleep from raw single-channel electroencephalogram data, employing novel features based on REM microstructures. Sleep stage classification was performed by means of random forest (RF) classifier, K-nearest neighbour (K-NN) classifier and random Under sampling boosted trees (RUSBoost); the classifiers were trained using a set of published and novel features. REM detection accuracy ranges from 89% to 92.7%, and the classifiers achieved a F-1 score (REM class) of about 0.83 (RF), 0.80 (K-NN), and 0.70 (RUSBoost). These methods provide encouraging outcomes in automatic sleep scoring and REM detection based on raw single-channel electroencephalogram, assessing the feasibility of a home sleep monitoring device with fewer channels

Drivers’ drowsiness detection based on an optimized random forest classification and single-channel electroencephalogram

The state of functioning (posture) of a driver at the wheel of a car involves a complex set of psychological, physiological, and physical parameters. This combination induces fatigue, which manifests itself in repeated yawning, stinging eyes, a frozen gaze, a stiff and painful neck, back pain, and other signs. The driver may fight fatigue for a few moments, but it inevitably leads to drowsiness, periods of micro-sleep, and then falling asleep. At the first signs of drowsiness, the risk of an accident becomes immense. In Morocco, drowsiness at the wheel is the cause of 1/3 of fatal accidents on the freeways. Thus, in this paper, a new hybrid data analysis and an efficient machine learning algorithm are designed to detect the drowsiness of drivers who spend most of their time behind the wheel over long distances (older than 35 years). This analysis is based on a single channel of electroencephalogram (EEG) recordings using time, frequency fast Fourier transform (FFT), and power spectral density (PSD) analysis. To distinguish between the two states of alertness and drowsiness, several features were extracted from each domain (time, FFT, and PSD), and subjected to different classifier architectures to conduct a general comparison and achieve the highest detection accuracy (98.5%) and best time consumption (13 milliseconds)

Physiological Approach To Characterize Drowsiness In Simulated Flight Operations During Window Of Circadian Low

Drowsiness is a psycho-physiological transition from awake towards falling sleep and its detection is crucial in aviation industries. It is a common cause for pilot’s error due to unpredictable work hours, longer flight periods, circadian disruption, and insufficient sleep. The pilots’ are prone towards higher level of drowsiness during window of circadian low (2:00 am- 6:00 am). Airplanes require complex operations and lack of alertness increases accidents. Aviation accidents are much disastrous and early drowsiness detection helps to reduce such accidents. This thesis studied physiological signals during drowsiness from 18 commercially-rated pilots in flight simulator. The major aim of the study was to observe the feasibility of physiological signals to predict drowsiness. In chapter 3, the spectral behavior of electroencephalogram (EEG) was studied via power spectral density and coherence. The delta power reduced and alpha power increased significantly (

Convolutional Neural Networks for Sleep Stage Scoring on a Two-Channel EEG Signal

This is a pre-print of an article published in Soft Computing. The final authenticated version is available online at: https://doi.org/10.1007/s00500-019-04174-1[Abstract] Sleeping problems have become one of the major diseases all over the world. To tackle this issue, the basic tool used by specialists is the Polysomnogram, which is a collection of different signals recorded during sleep. After its recording, the specialists have to score the different signals according to one of the standard guidelines. This process is carried out manually, which can be highly time consuming and very prone to annotation errors. Therefore, over the years, many approaches have been explored in an attempt to support the specialists in this task. In this paper, an approach based on convolutional neural networks is presented, where an in-depth comparison is performed in order to determine the convenience of using more than one signal simultaneously as input. Additionally, the models were also used as parts of an ensemble model to check whether any useful information can be extracted from signal processing a single signal at a time which the dual-signal model cannot identify. Tests have been performed by using a well-known dataset called expanded sleep-EDF, which is the most commonly used dataset as benchmark for this problem. The tests were carried out with a leave-one-out cross-validation over the patients, which ensures that there is no possible contamination between training and testing. The resulting proposal is a network smaller than previously published ones, but which overcomes the results of any previous models on the same dataset. The best result shows an accuracy of 92.67% and a Cohen’s Kappa value over 0.84 compared to human experts.Instituto de Salud Carlos III; PI17/01826Xunta de Galicia; ED431D 2017/23Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431G/0

Classification of EEG signals of user states in gaming using machine learning

In this research, brain activity of user states was analyzed using machine learning algorithms. When a user interacts with a computer-based system including playing computer games like Tetris, he or she may experience user states such as boredom, flow, and anxiety. The purpose of this research is to apply machine learning models to Electroencephalogram (EEG) signals of three user states -- boredom, flow and anxiety -- to identify and classify the EEG correlates for these user states. We focus on three research questions: (i) How well do machine learning models like support vector machine, random forests, multinomial logistic regression, and k-nearest neighbor classify the three user states -- Boredom, Flow, and Anxiety? (ii) Can we distinguish the flow state from other user states using machine learning models? (iii) What are the essential components of EEG signals for classifying the three user states? To extract the critical components of EEG signals, a feature selection method known as minimum redundancy and maximum relevance method was implemented. An average accuracy of 85 % is achieved for classifying the three user states by using the support vector machine classifier --Abstract, page iii

HyCLASSS: A Hybrid Classifier for Automatic Sleep Stage Scoring

Automatic identification of sleep stage is an important step in a sleep study. In this paper, we propose a hybrid automatic sleep stage scoring approach, named HyCLASSS, based on single channel electroencephalogram (EEG). HyCLASSS, for the first time, leverages both signal and stage transition features of human sleep for automatic identification of sleep stages. HyCLASSS consists of two parts: A random forest classifier and correction rules. Random forest classifier is trained using 30 EEG signal features, including temporal, frequency, and nonlinear features. The correction rules are constructed based on stage transition feature, importing the continuity property of sleep, and characteristic of sleep stage transition. Compared with the gold standard of manual scoring using Rechtschaffen and Kales criterion, the overall accuracy and kappa coefficient applied on 198 subjects has reached 85.95% and 0.8046 in our experiment, respectively. The performance of HyCLASS compared favorably to previous work, and it could be integrated with sleep evaluation or sleep diagnosis system in the future