Auditory evoked potentials in patients with major depressive disorder measured by Emotiv system

Wang, Dongcui; Mo, Fongming; Yang-de, Zhang; Yang, Chao; Liu, Jun; Chen, Zhencheng; Zhao, Jinfeng

doi:10.3233/bme-151385

Cited by 11 publications

(6 citation statements)

References 26 publications

(30 reference statements)

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“…The classification accuracy was evaluated using a two-class linear support vector machine (SVM) classifier (Alimardani et al, 2018a; Orru et al, 2012) with a leave-one-out cross-validation (LOOCV) method for each feature set (Shim et al, 2016). To compute LOOCV, the only one subject was used for validating the model and remaining subjects (N-1) were used for training of the model (Al-Kaysi et al, 2017; Wang et al, 2015).…”

Section: Methodsmentioning

confidence: 99%

Machine-learning-based classification between post-traumatic stress disorder and major depressive disorder using P300 features

Shim

Jin

et al. 2019

NeuroImage: Clinical

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BackgroundThe development of optimal classification criteria for specific mental disorders which share similar symptoms is an important issue for precise diagnosis. We investigated whether P300 features in both sensor-level and source-level could be effectively used to classify post-traumatic stress disorder (PTSD) and major depressive disorder (MDD).MethodEEG signals were recorded from fifty-one PTSD patients, 67 MDD patients, and 39 healthy controls (HCs) while performing an auditory oddball task. Amplitude and latency of P300 were evaluated, and the current source analysis of P300 components was conducted using sLORETA. Finally, we classified two groups using machine-learning methods with both sensor- and source-level features. Moreover, we checked the comorbidity effects using the same approaches (PTSD-mono diagnosis (PTSDm, n = 28) and PTSD-comorbid diagnosis (PTSDc, n = 23)).ResultsPTSD showed significantly reduced P300 amplitudes and prolonged latency compared to HCs and MDD. Moreover, PTSD showed significantly reduced source activities, and the source activities were significantly correlated with symptoms of depression and anxiety. Also, the best classification accuracy at each pair was as follows: 80.00% (PTSD-HCs), 67.92% (MDD-HCs), 70.34% (PTSD-MDD), 82.09% (PTSDm-HCs), 71.58% (PTSDm-MDD), 82.56% (PTSDc-HCs), and 76.67% (PTSDc- MDD).ConclusionSince abnormal P300 reflects pathophysiological characteristics of PTSD, PTSD patients were well-discriminated from MDD and HCs when using P300 features. Thus, altered P300 characteristics in both sensor- and source-level may be useful biomarkers to diagnosis PTSD.

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Section: Methodsmentioning

confidence: 99%

Machine-learning-based classification between post-traumatic stress disorder and major depressive disorder using P300 features

Shim

Jin

et al. 2019

NeuroImage: Clinical

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“…EEG was sampled by Emotiv Epoc 128 Hz system (using saline based electrodes) 38,39 (Fig. 1) parallel to BIS recordings 40 .…”

Section: Methodsmentioning

confidence: 99%

A New Index of Coordinated Posterior and Anterior Evoked EEG to Detect Recall Under Sedation – A Pilot Study

Shahaf

Hare

Baker

et al. 2019

Sci Rep

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EEG-based technologies may be limited in identifying recall under sedation (RUS). We developed a novel index, posteriorization/anteriorization (P/A) index, based on auditory evoked EEG signal and assessed whether it could differentiate between patients with or without RUS. Methods: EEG and BIS were sampled from 3 groups: 1. Patients undergoing sedation (n = 26); 2. Awake volunteers (n = 13, positive control for recall) 3. Patients undergoing general anesthesia (GA, n = 12, negative control for recall). In recovery, recall was assessed using the BRICE questionnaire. Of the 26 sedated patients, 12 experienced recall. Both The P/A index and BIS differentiated between patients with recall and no recall. However, BIS differentiation may have been sensitive to the main drug used for sedation (midazolam vs. propofol) and the P/A index did not show similar drug-based sensitivity. Furthermore, only BIS results were correlated with EMG. Conclusion: This pilot study provided support for the association between P/A index and recall after sedation. Further research is needed in integrating the index into clinical use: (1) it should be derived by an easy-to-use EEG system with a better signal-to-noise ratio; (2) its applicability to other drugs must be shown.

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“…The EEG recordings were carried out using the Emotiv EPOC device. While the device has been shown to have similar results to more expensive clinical grade systems [17][18][19][20][21], we nonetheless performed validations by comparing results to simultaneous recordings from the clinical grade Neuroscan device along multiple dimensions with highly correlated results [16]. Survey methodology and sampling are described in more detail in a companion paper [16].…”

Section: Sampling Survey and Eeg Recordingmentioning

confidence: 99%

Modernization, wealth and the emergence of strong alpha oscillations in the human EEG

Parameshwaran¹,

Thiagarajan²

2017

Preprint

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Oscillations in the alpha range (8-15 Hz) have been found to appear prominently in the EEG signal when people are awake with their eyes closed, and since their discovery have been considered a fundamental cerebral rhythm. While the mechanism of this oscillation continues to be debated, it has been shown to bear positive relation to memory capacity, attention and a host of other cognitive outcomes. Here we show that this feature is largely undetected in the EEG of adults without post-primary education and access to modern technologies. Furthermore, we show that the spatial extent and energy of the oscillation have wide variation, with energy ranging over a thousand fold across the breath of humanity with no centralizing mean. This represents a divergence in a fundamental functional characteristic of an organ demonstrating both that modernization has had a profound influence on brain dynamics and that a meaningful 'average' human brain does not exist in a dynamical sense.

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Auditory evoked potentials in patients with major depressive disorder measured by Emotiv system

Cited by 11 publications

References 26 publications

Machine-learning-based classification between post-traumatic stress disorder and major depressive disorder using P300 features

Machine-learning-based classification between post-traumatic stress disorder and major depressive disorder using P300 features

A New Index of Coordinated Posterior and Anterior Evoked EEG to Detect Recall Under Sedation – A Pilot Study

Modernization, wealth and the emergence of strong alpha oscillations in the human EEG

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