Subject-Dependent and Subject-Independent Classification of Mental Arithmetic and Silent Reading Tasks

Arslan, Mustafa Turan; Eraldemir, Server Göksel; Yıldırım, Esen

doi:10.29137/umagd.348871

Cited by 9 publications

(6 citation statements)

References 31 publications

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“…EEG signals are nonstationary and nonlinear signals, similar to many other physiological signals [20]. To analyze these signals, linear and nonlinear features are typically used, such as the power spectrum density, Lempel-Ziv complexity, variance, mobility, fluctuations, Higuchi fractal, approximate entropy, Kolmogorov entropy, correlation dimension, Lyapunov exponent, and permutation entropy [2][8] [9].…”

mentioning

confidence: 99%

Enhancing EEG-Based Classification of Depression Patients Using Spatial Information

Jiang

Tang

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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confidence: 99%

Enhancing EEG-Based Classification of Depression Patients Using Spatial Information

Jiang

Tang

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…BCI is a subject-dependent system where each person had different characteristic and resulted in better accuracy values if each individual was trained and tested on their recordings. [1314] The 28 EEG signals consist of the drowsy and awake states were collected from two healthy individuals (male and female, age ranged from 20 to 25 years old who did not experience insomnia) using EMOTIV Epoc+ with 14 channel electrodes and two references (CMS/DRL noise cancellation configuration in P3/P4 locations). The sampling rate of EMOTIV Epoc+ device is 128 sampling per sequence with resolution 14 bits 1 least significant bit = 0.5 μV (16-bit ADC, 2-bit instrumental noise floor discarded) and connected by Bluetooth.…”

Section: Methodsmentioning

confidence: 99%

Drowsiness analysis using common spatial pattern and extreme learning machine based on electroencephalogram signal

Rahma

Rahmatillah

2019

J Med Signals Sens

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An alarm system has become essential to prevent someone from drowsiness while driving, considering the high incidence due to fatigue or drowsiness. This study offered an alternative to overcome all the limitations provided by the conventional system to detect sleepiness based on the driver's brain electrical activity using wearable electroencephalogram (EEG), which is lighter and easy to use. The EEG signals were collected using EMOTIV Epoc + and then were decomposed into narrowband frequency, such as delta, theta, alpha, and beta using DWT. The relative power, as the result of feature extraction, then were processed further by calculating its variance using the common spatial pattern (CSP) method to optimize the accuracy of extreme learning machine (ELM). Comparison of relative power between awake and drowsy state showed that during the drowsy state, theta-wave, alpha-wave, and beta-wave were tend to be higher than in the awake state. However, despite with the help of ELM, the accuracy was not too high (below 87%). The feature extraction which continued by calculating its variance using CSP algorithm before classified by ELM obtained a high accuracy, even with small amount of data training. This showed that CSP combining with ELM could be useful to shorten the time in training/calibration session, yet still, obtained high accuracy in classifying the awake state and drowsy state. The overall average accuracy of testing ranged from 91.67% to 93.75%. This study could increase the ability of EEG in detecting drowsiness that is important to prevent the risk caused by driving in a drowsy state.

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“…In research involving the application of classification algorithms to the recognition of human mental states (such as emotions, mental disorders and motor imagery), two basic schemes for classification exist: subject-dependent and subject-independent strategies. The subject-dependent algorithms require a classifier to be trained for each subject, whereas subject-independent algorithms train the classifier using data from several subjects [61]. It should be noted that depression recognition is considered a subject-independent classification case.…”

Section: E Classifiersmentioning

confidence: 99%

Multimodal Mild Depression Recognition Based on EEG-EM Synchronization Acquisition Network

Zhu

Wang

et al. 2019

IEEE Access

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In this paper, we used electroencephalography (EEG)-eye movement (EM) synchronization acquisition network to simultaneously record both EEG and EM physiological signals of the mild depression and normal controls during free viewing. Then, we consider a multimodal feature fusion method that can best discriminate between mild depression and normal control subjects as a step toward achieving our longterm aim of developing an objective and effective multimodal system that assists doctors during diagnosis and monitoring of mild depression. Based on the multimodal denoising autoencoder, we use two feature fusion strategies (feature fusion and hidden layer fusion) for fusion of the EEG and EM signals to improve the recognition performance of classifiers for mild depression. Our experimental results indicate that the EEG-EM synchronization acquisition network ensures that the recorded EM and EEG data require that both the data streams are synchronized with millisecond precision, and both fusion methods can improve the mild depression recognition accuracy, thus demonstrating the complementary nature of the modalities. Compared with the unimodal classification approach that uses only EEG or EM, the feature fusion method slightly improved the recognition accuracy by 1.88%, while the hidden layer fusion method significantly improved the classification rate by up to 7.36%. In particular, the highest classification accuracy achieved in this paper was 83.42%. These results indicate that the multimodal deep learning approaches with input data using a combination of EEG and EM signals are promising in achieving real-time monitoring and identification of mild depression.

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Subject-Dependent and Subject-Independent Classification of Mental Arithmetic and Silent Reading Tasks

Cited by 9 publications

References 31 publications

Enhancing EEG-Based Classification of Depression Patients Using Spatial Information

Enhancing EEG-Based Classification of Depression Patients Using Spatial Information

Drowsiness analysis using common spatial pattern and extreme learning machine based on electroencephalogram signal

Multimodal Mild Depression Recognition Based on EEG-EM Synchronization Acquisition Network

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