Since the introduction of the first edition of International Classification of Sleep Disorders: Diagnostic and Coding Manual(ICSD-1)in 1990,national and international meetings were held to openly discuss the ongoing developments of sleep disorders and a new International Classification of Sleep Disorders: Diagnostic and Coding Manual(ICSD-2)was published in 2005.Compared with ICSD-1,the classification of ICSD-2 was developed in a manner compatible with new International Classification of Diseases(ICD-9 and ICD-10)and formed a coordinated system of International Classification of Diseases.The updated version was characterized by the significant improvements of its logicality and clinical practicability,and was more consistent with the International Classification of Disease.The contents of ICSD-2 were introduced in this article.

Brief introduction to second edition of International Classification of Sleep Disorders:Diagnostic and Coding Manual

Drowsiness is a leading cause of traffic and industrial accidents, costing lives and productivity. Electroencephalography (EEG) signals can reflect awareness and attentiveness, and low-cost consumer EEG headsets are available on the market. The use of these devices as drowsiness detectors could increase the accessibility of safety and productivity-enhancing devices for small businesses and developing countries. We conducted a systemic review of currently available, low-cost, consumer EEG-based drowsiness detection systems. We sought to determine whether consumer EEG headsets could be reliably utilized as rudimentary drowsiness detection systems. We included documented cases describing successful drowsiness detection using consumer EEG-based devices, including the Neurosky MindWave, InteraXon Muse, Emotiv Epoc, Emotiv Insight, and OpenBCI. Of 46 relevant studies, ~27 reported an accuracy score. The lowest of these was the Neurosky Mindwave, with a minimum of 31%. The second lowest accuracy reported was 79.4% with an OpenBCI study. In many cases, algorithmic optimization remains necessary. Different methods for accuracy calculation, system calibration, and different definitions of drowsiness made direct comparisons problematic. However, even basic features, such as the power spectra of EEG bands, were able to consistently detect drowsiness. Each specific device has its own capabilities, tradeoffs, and limitations. Widely used spectral features can achieve successful drowsiness detection, even with low-cost consumer devices; however, reliability issues must still be addressed in an occupational context.

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A Systemic Review of Available Low-Cost EEG Headsets Used for Drowsiness Detection.

Many scientific studies have been concerned with building an automatic system to recognize emotions, and building such systems usually relies on brain signals. These studies have shown that brain signals can be used to classify many emotional states. This process is considered difficult, especially since the brain’s signals are not stable. Human emotions are generated as a result of reactions to different emotional states, which affect brain signals. Thus, the performance of emotion recognition systems by brain signals depends on the efficiency of the algorithms used to extract features, the feature selection algorithm, and the classification process. Recently, the study of electroencephalography (EEG) signaling has received much attention due to the availability of several standard databases, especially since brain signal recording devices have become available in the market, including wireless ones, at reasonable prices. This work aims to present an automated model for identifying emotions based on EEG signals. The proposed model focuses on creating an effective method that combines the basic stages of EEG signal handling and feature extraction. Different from previous studies, the main contribution of this work relies in using empirical mode decomposition/intrinsic mode functions (EMD/IMF) and variational mode decomposition (VMD) for signal processing purposes. Despite the fact that EMD/IMFs and VMD methods are widely used in biomedical and disease-related studies, they are not commonly utilized in emotion recognition. In other words, the methods used in the signal processing stage in this work are different from the methods used in literature. After the signal processing stage, namely in the feature extraction stage, two well-known technologies were used: entropy and Higuchi’s fractal dimension (HFD). Finally, in the classification stage, four classification methods were used—naive Bayes, k-nearest neighbor (k-NN), convolutional neural network (CNN), and decision tree (DT)—for classifying emotional states. To evaluate the performance of our proposed model, experiments were applied to a common database called DEAP based on many evaluation models, including accuracy, specificity, and sensitivity. The experiments showed the efficiency of the proposed method; a 95.20% accuracy was achieved using the CNN-based method.

Machine-Learning-Based Emotion Recognition System Using EEG Signals

As a subjectively psychological and physiological response to external stimuli, emotion is ubiquitous in our daily life. With the continuous development of the artificial intelligence and brain science, emotion recognition rapidly becomes a multiple discipline research field through EEG signals. This paper investigates the relevantly scientific literature in the past five years and reviews the emotional feature extraction methods and the classification methods using EEG signals. Commonly used feature extraction analysis methods include time domain analysis, frequency domain analysis, and time-frequency domain analysis. The widely used classification methods include machine learning algorithms based on Support Vector Machine (SVM), k-Nearest Neighbor (KNN), Naive Bayes (NB), etc., and their classification accuracy ranges from 57.50% to 95.70%. The classification accuracy of the deep learning algorithms based on Neural Network (NN), Long and Short-Term Memory (LSTM), and Deep Belief Network (DBN) ranges from 63.38% to 97.56%.

Review of the emotional feature extraction and classification using EEG signals

EEG emotion recognition model based on the LIBSVM classifier

Drowsiness has symptoms which are itchy eyes, slow eye blink movement, smaller pupils, yawning and even trembling body. But the driver ignores it when the body send one of those signals often. The impacts which can occur to the driver, such as make incorrect decision while driving, could happen and lead to the most car accident reason. Therefore, the system which can provide an alarm when the driver feels drowsiness, fatigue or even microsleep is required. The way to detect microsleep when it occurs is to use the Electroencephalograph (EEG) brainwave. The system uses the one channel EEG Sensor device developed by Neurosky Mindwave which can provide eight brainwave signal such as Delta, Theta, Low Alpha, High Alpha, Low Beta, High Beta, Low Gamma, and Mid Gamma. On the other hand, attention and relaxation value can be generated as well. This prototype system tested by the car driver achieved its purpose of detecting microsleep event and alerting the driver by the alarm.

EEG-based microsleep detector using microcontroller

Research on the correlation of EEG signals to emotions based on high/low arousal and valence, has been done before. Some research using the Eigen-Emotion Pattern Kernel method and the Support Vector Machine. The others using the Higuchi Fractal Dimension (FD) Spectrum, the Multifractal Detrended Fluctuation Analysis (MDFA) and the Hidden Markov Model (HMM), but the accuracy is not too good. This research uses Wavelet Decomposition and k-Nearest Neighbor to improve accuracy. The results show that the optimum k values of the k-Nearest Neighbor parameters for this research are 21. Valence's classification accuracy results using Wavelet and k-NN, compared with previous research has the same relative accuracy, ie 57.5%. While the result of arousal classification accuracy using wavelet and k-NN is 64.0%, better than previous research.

EEG-Based Emotion Classification Using Wavelet Decomposition and K-Nearest Neighbor

Many research on how the human brain works has been done in the last century. The use of electroencephalogram signal generated from quantifying the brain wave have been developed in many areas including the development of brain computer interface (BCI) concept. One type of BCI that interesting for the future use is motor imagery (MI) based-BCI which only requiring imagination of a person to control an object. This study proposed a feature extraction in eight different channels using discrete wavelet (DWT) coefficients. The wavelet coefficient is transformed to frequency domain using discrete fourier transform (DFT) and then average power spectrum is calculated. Level 5 of detail component of the DWT is chosen because from 512Hz sampling frequency (8 - 16Hz), it resemble mu rhythm of brain wave (8 - 12Hz) which affected from motor imagery activity. The classification of three classes, which are imagination of right body movement, left movement, and random word using multiclass support vector machine (SVM) shows a promising result with sensitivity of 96.88%, 86.12% and 52.78% from three different subjects.

Three-Class Classification of EEG Signals Using Support Vector Machine Methods

The degradation of teak forest plantations in Java that are managed by Perum Perhutani (PP) continues to happen, and this is caused by some risk factors such as illegal logging, grazing, forest fire, and encroachment. However, these risk factors have not been considered by PP notably in annual allowable cut (AAC) determination of yield regulation. Therefore, the AAC value could be overestimated. The research was aimed at evaluating the method of AAC determination and proposing an alternative method that considers the risk factors. This research was conducted with a series of data analysis approach from the data on five planning periods. The research result showed that forest damage occurs in varied situations. On average, the rate of deforestation accounted for 0.8% per annum. The calculation of AAC by considering the rate of damage risk in normal condition approximately made up 70.8%. Thus, compared to another method without considering damage risk rate, overestimation constituted 29.2%. In brief, this had an impact on the decline of timber stock.

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Evaluation of Annual Allowable Cut (AAC) Determination of Teak Forest Plantations in Perum Perhutani, Indonesia

In the past few decades, biomedical signals have played important roles in assisting diagnosis for medical purposes. After the rose of brain-computer interfaces (BCI) and human-machine interaction (HMI) concept, biomedical signals such as electroencephalograph (EEG) and electrooculograph (EOG) begun to be implemented in control and communication systems. EOG, the signal resulted from eye movement, has been used to design various applications from drowsiness detection to virtual keyboard control. The key of the system developed from EOG signal is the detection system for every eye movement. In this study, a sliding window technique is proposed to make eye movement patterns easier be formulated and using overlap window to avoid local extrema when computing the feature. Evaluation of this method shows that combination of 0.5 s-window length and 25% overlap give 17% and 1% false discovery rate (FDR) in vertical and horizontal channel while the true positive rate (TPR) in both channel is 98% The combination of automatic-window and 25% overlap give a better accuracy with 99% and 100% TPR in the two direction while the FDRs are 22% and 1%.

Catur Atmaji

Papers

EEG-based microsleep detector using microcontroller

EEG-Based Emotion Classification Using Wavelet Decomposition and K-Nearest Neighbor

Three-Class Classification of EEG Signals Using Support Vector Machine Methods

Evaluation of Annual Allowable Cut (AAC) Determination of Teak Forest Plantations in Perum Perhutani, Indonesia

Sliding window method for eye movement detection based on electrooculogram signal