Displaying publications 81 - 100 of 245 in total

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  1. Fulltext A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability
    Awais M, Badruddin N, Drieberg M
    Sensors (Basel), 2017 Aug 31;17(9).
    PMID: 28858220 DOI: 10.3390/s17091991
    Driver drowsiness is a major cause of fatal accidents, injury, and property damage, and has become an area of substantial research attention in recent years. The present study proposes a method to detect drowsiness in drivers which integrates features of electrocardiography (ECG) and electroencephalography (EEG) to improve detection performance. The study measures differences between the alert and drowsy states from physiological data collected from 22 healthy subjects in a driving simulator-based study. A monotonous driving environment is used to induce drowsiness in the participants. Various time and frequency domain feature were extracted from EEG including time domain statistical descriptors, complexity measures and power spectral measures. Features extracted from the ECG signal included heart rate (HR) and heart rate variability (HRV), including low frequency (LF), high frequency (HF) and LF/HF ratio. Furthermore, subjective sleepiness scale is also assessed to study its relationship with drowsiness. We used paired t-tests to select only statistically significant features (p < 0.05), that can differentiate between the alert and drowsy states effectively. Significant features of both modalities (EEG and ECG) are then combined to investigate the improvement in performance using support vector machine (SVM) classifier. The other main contribution of this paper is the study on channel reduction and its impact to the performance of detection. The proposed method demonstrated that combining EEG and ECG has improved the system's performance in discriminating between alert and drowsy states, instead of using them alone. Our channel reduction analysis revealed that an acceptable level of accuracy (80%) could be achieved by combining just two electrodes (one EEG and one ECG), indicating the feasibility of a system with improved wearability compared with existing systems involving many electrodes. Overall, our results demonstrate that the proposed method can be a viable solution for a practical driver drowsiness system that is both accurate and comfortable to wear.
    Matched MeSH terms: Electroencephalography
  2. Fulltext A review on EEG-based methods for screening and diagnosing alcohol use disorder
    Mumtaz W, Vuong PL, Malik AS, Rashid RBA
    Cogn Neurodyn, 2018 Apr;12(2):141-156.
    PMID: 29564024 DOI: 10.1007/s11571-017-9465-x
    The screening test for alcohol use disorder (AUD) patients has been of subjective nature and could be misleading in particular cases such as a misreporting the actual quantity of alcohol intake. Although the neuroimaging modality such as electroencephalography (EEG) has shown promising research results in achieving objectivity during the screening and diagnosis of AUD patients. However, the translation of these findings for clinical applications has been largely understudied and hence less clear. This study advocates the use of EEG as a diagnostic and screening tool for AUD patients that may help the clinicians during clinical decision making. In this context, a comprehensive review on EEG-based methods is provided including related electrophysiological techniques reported in the literature. More specifically, the EEG abnormalities associated with the conditions of AUD patients are summarized. The aim is to explore the potentials of objective techniques involving quantities/features derived from resting EEG, event-related potentials or event-related oscillations data.
    Matched MeSH terms: Electroencephalography
  3. Elucidating the visual phenomena in epilepsy: A mini review
    Akyuz E, Arulsamy A, Hasanli S, Yilmaz EB, Shaikh MF
    Epilepsy Res, 2023 Feb;190:107093.
    PMID: 36652852 DOI: 10.1016/j.eplepsyres.2023.107093
    Epilepsy is one of the most recognizable neurological diseases, globally. Epilepsy may be accompanied by various complications, including vision impairments, which may severely impact one's quality of life. These visual phenomena may occur in the preictal, ictal and/or postictal periods of seizures. Examples of epilepsy associated visual phenomena include visual aura, visual hallucinations, transient visual loss and amaurosis (blindness). These ophthalmologic signs/symptoms of epilepsy may be temporary or permanent and may vary depending of the type of epilepsy and location of the seizure foci (occipital or temporal lobe). Some visual phenomena may even be utilized to diagnose the epilepsy type, although solely depending on visual symptoms for diagnosis may lead to mistreatment. Some antiseizure medications (ASMs) may also contribute to certain visual disturbances, thereby impacting its therapeutic efficiency for patients with epilepsy (PWE). Although the development of visual comorbidities has been observed diversely among PWE, there may still be a lack of understanding on their relevance and manifestation in epilepsy, which may contribute to the rate of misdiagnosis and the current scarcity in therapeutic relieve. Therefore, this mini narrative review aimed to discuss the common epilepsy associated visual phenomena, based on the available literature. This review also showcased the relationship between the type of visual complications and the site of seizure onset, as well as compared the visual phenomena between occipital lobe epilepsy and temporal lobe epilepsy. Evaluation of these findings may be crucial in reducing the risk of permanent seizure/epilepsy related vision deficits among PWE.
    Matched MeSH terms: Electroencephalography
  4. Fulltext Emotion Recognition Using Eye-Tracking: Taxonomy, Review and Current Challenges
    Lim JZ, Mountstephens J, Teo J
    Sensors (Basel), 2020 Apr 22;20(8).
    PMID: 32331327 DOI: 10.3390/s20082384
    The ability to detect users' emotions for the purpose of emotion engineering is currently one of the main endeavors of machine learning in affective computing. Among the more common approaches to emotion detection are methods that rely on electroencephalography (EEG), facial image processing and speech inflections. Although eye-tracking is fast in becoming one of the most commonly used sensor modalities in affective computing, it is still a relatively new approach for emotion detection, especially when it is used exclusively. In this survey paper, we present a review on emotion recognition using eye-tracking technology, including a brief introductory background on emotion modeling, eye-tracking devices and approaches, emotion stimulation methods, the emotional-relevant features extractable from eye-tracking data, and most importantly, a categorical summary and taxonomy of the current literature which relates to emotion recognition using eye-tracking. This review concludes with a discussion on the current open research problems and prospective future research directions that will be beneficial for expanding the body of knowledge in emotion detection using eye-tracking as the primary sensor modality.
    Matched MeSH terms: Electroencephalography
  5. Brain waves spectral analysis of human responses to odorous and non-odorous substances: a preliminary study
    Chow XH, Ting CM, Wan Hamizan AK, Zahedi FD, Tan HJ, Remli R, et al.
    J Laryngol Otol, 2024 Mar;138(3):301-309.
    PMID: 37259908 DOI: 10.1017/S0022215123000919
    OBJECTIVE: The aim of this study was to identify the potential electrophysiological biomarkers of human responses by comparing the electroencephalogram brain wave changes towards lavender versus normal saline in a healthy human population.

    METHOD: This study included a total of 44 participants without subjective olfactory disturbances. Lavender and normal saline were used as the olfactory stimulant and control. Electroencephalogram was recorded and power spectra were analysed by the spectral analysis for each alpha, beta, delta, theta and gamma bandwidth frequency upon exposure to lavender and normal saline independently.

    RESULTS: The oscillatory brain activities in response to the olfactory stimulant indicated that the lavender smell decreased the beta activity in the left frontal (F7 electrode) and central region (C3 electrode) with a reduction in the gamma activity in the right parietal region (P4 electrode) (p < 0.05).

    CONCLUSION: Olfactory stimulants result in changes of electrical brain activities in different brain regions, as evidenced by the topographical brain map and spectra analysis of each brain wave.

    Matched MeSH terms: Electroencephalography
  6. Fulltext Age-dependent Electroencephalographic Differences in the Genetic Absence Epilepsy Rats from Strasbourg (GAERS) Model of Absence Epilepsy
    Islam MR, Abdullah JM
    Malays J Med Sci, 2014 Dec;21(Spec Issue):34-40.
    PMID: 25941461 MyJurnal
    Genetic Absence Epilepsy Rats from Strasbourg (GAERS) are a prognostic genetic model of absence epilepsy. This model displays the electro-clinical, behavioural, and pharmacological features of absence seizures. Although GAERS share typical characteristics, including spike-and-wave discharges (SWDs) in the electroencephalography (EEG), age-dependent studies with these animals have not yet been reported. The aim of the present study is to perform a systematic comparison contrasting the SWDs of young and older GAERS, in terms of the number, duration, frequency, and waveform morphology of the discharges, as well as the pre-SWD EEG characteristics, using identical measurement and analysis techniques. The number, cumulative total duration and mean duration of SWDs were significantly higher in young GAERS (4 to 6 months) compared to older GAERS (12 to 14 months). Furthermore, the SWD spectra and average SWD waveforms indicated that a single cycle of the SWD contains more energy in faster components, such as increased spikes and higher power, in the SWDs of the young GAERS. Additionally, older GAERS showed weak amplitude spikes in SWDs and higher power pre-SWDs. These clear morphological differences in the EEGs of young and older GAERS rats should be further examined in future studies that explore new dimensions of genetic absence epilepsy.
    Matched MeSH terms: Electroencephalography
  7. Telemetric EEG and the rat: a guide for neuroscientists
    Abdullah JM, Rafiqul Islam M
    Malays J Med Sci, 2012 Oct;19(4):1-5.
    PMID: 23613643
    Telemetric EEG in the rat's brain has been used for experiments which tests the effects of an antiepileptic compound on it's antiseizures activity. A simple classification correlating epileptiform discharge and Racine's behavioral activity is discussed.
    Matched MeSH terms: Electroencephalography
  8. Fulltext The Effect of Sex on the Electropsychological Process of Emotional Arousal Intensity
    Yusoff N, Anuar NN, Reza MF
    Malays J Med Sci, 2018 May;25(3):103-110.
    PMID: 30899191 MyJurnal DOI: 10.21315/mjms2018.25.3.10
    Background: Sex is a psychobiological factor that is important in the process of emotion. This study determines the effect of sex on the electropsychological process of various intensities of emotional arousal.

    Methods: In the Event-related Potential (ERP) session, electroencephalographic (EEG) data was recorded for 90 participants, 60% of whom were females. The participants responded to 30 universal emotional pictures, randomly chosen from the International Affective Picture System (IAPS), which were classified as invoking high, moderate, and low intensity of emotional arousal.

    Results: From the analysis of variance of two-way mixed design, the interaction between sex and emotional intensity was observed in the occipital regions (O2), indexed by the amplitude of P300 and N200 components. Males exhibited higher amplitude of P300 and N200 components (in the occipital region) as responded to high and low emotional arousal stimuli than females.

    Conclusion: Sex is a fundamental factor that modulates psychological states in reaction to emotional stimuli.

    Matched MeSH terms: Electroencephalography
  9. Fulltext Reduction of the dimensionality of the EEG channels during scoliosis correction surgeries using a wavelet decomposition technique
    Al-Kadi MI, Reaz MB, Ali MA, Liu CY
    Sensors (Basel), 2014;14(7):13046-69.
    PMID: 25051031 DOI: 10.3390/s140713046
    This paper presents a comparison between the electroencephalogram (EEG) channels during scoliosis correction surgeries. Surgeons use many hand tools and electronic devices that directly affect the EEG channels. These noises do not affect the EEG channels uniformly. This research provides a complete system to find the least affected channel by the noise. The presented system consists of five stages: filtering, wavelet decomposing (Level 4), processing the signal bands using four different criteria (mean, energy, entropy and standard deviation), finding the useful channel according to the criteria's value and, finally, generating a combinational signal from Channels 1 and 2. Experimentally, two channels of EEG data were recorded from six patients who underwent scoliosis correction surgeries in the Pusat Perubatan Universiti Kebangsaan Malaysia (PPUKM) (the Medical center of National University of Malaysia). The combinational signal was tested by power spectral density, cross-correlation function and wavelet coherence. The experimental results show that the system-outputted EEG signals are neatly switched without any substantial changes in the consistency of EEG components. This paper provides an efficient procedure for analyzing EEG signals in order to avoid averaging the channels that lead to redistribution of the noise on both channels, reducing the dimensionality of the EEG features and preparing the best EEG stream for the classification and monitoring stage.
    Matched MeSH terms: Electroencephalography/instrumentation*; Electroencephalography/methods*
  10. EEG spectral analysis on Muslim prayers
    Doufesh H, Faisal T, Lim KS, Ibrahim F
    Appl Psychophysiol Biofeedback, 2012 Mar;37(1):11-8.
    PMID: 21965118 DOI: 10.1007/s10484-011-9170-1
    This study investigated the proposition of relaxation offered by performing the Muslim prayers by measuring the alpha brain activity in the frontal (F3-F4), central (C3-C4), parietal (P3-P4), and occipital (O1-O2) electrode placements using the International 10-20 System. Nine Muslim subjects were asked to perform the four required cycles of movements of Dhuha prayer, and the EEG were subsequently recorded with open eyes under three conditions, namely, resting, performing four cycles of prayer while reciting the specific verses and supplications, and performing four cycles of acted salat condition (prayer movements without any recitations). Analysis of variance (ANOVA) tests revealed that there were no significant difference in the mean alpha relative power (RP(α)) between the alpha amplitude in the Dhuha prayer and the acted conditions in all eight electrode positions. However, the mean RP(α) showed higher alpha amplitude during the prostration position of the Dhuha prayer and acted condition at the parietal and occipital regions in comparison to the resting condition. Findings were similar to other studies documenting increased alpha amplitude in parietal and occipital regions during meditation and mental concentration. The incidence of increased alpha amplitude suggested parasympathetic activation, thus indicating a state of relaxation. Subsequent studies are needed to delineate the role of mental concentration, and eye focus, on alpha wave amplitude while performing worshipping acts.
    Matched MeSH terms: Electroencephalography/instrumentation; Electroencephalography/methods*
  11. Electroencephalographic Motor Imagery Brain Connectivity Analysis for BCI: A Review
    Hamedi M, Salleh ShH, Noor AM
    Neural Comput, 2016 06;28(6):999-1041.
    PMID: 27137671 DOI: 10.1162/NECO_a_00838
    Recent research has reached a consensus on the feasibility of motor imagery brain-computer interface (MI-BCI) for different applications, especially in stroke rehabilitation. Most MI-BCI systems rely on temporal, spectral, and spatial features of single channels to distinguish different MI patterns. However, no successful communication has been established for a completely locked-in subject. To provide more useful and informative features, it has been recommended to take into account the relationships among electroencephalographic (EEG) sensor/source signals in the form of brain connectivity as an efficient tool of neuroscience. In this review, we briefly report the challenges and limitations of conventional MI-BCIs. Brain connectivity analysis, particularly functional and effective, has been described as one of the most promising approaches for improving MI-BCI performance. An extensive literature on EEG-based MI brain connectivity analysis of healthy subjects is reviewed. We subsequently discuss the brain connectomes during left and right hand, feet, and tongue MI movements. Moreover, key components involved in brain connectivity analysis that considerably affect the results are explained. Finally, possible technical shortcomings that may have influenced the results in previous research are addressed and suggestions are provided.
    Matched MeSH terms: Electroencephalography/methods*; Electroencephalography/trends
  12. EEG based brain source localization comparison of sLORETA and eLORETA
    Jatoi MA, Kamel N, Malik AS, Faye I
    Australas Phys Eng Sci Med, 2014 Dec;37(4):713-21.
    PMID: 25359588 DOI: 10.1007/s13246-014-0308-3
    Human brain generates electromagnetic signals during certain activation inside the brain. The localization of the active sources which are responsible for such activation is termed as brain source localization. This process of source estimation with the help of EEG which is also known as EEG inverse problem is helpful to understand physiological, pathological, mental, functional abnormalities and cognitive behaviour of the brain. This understanding leads for the specification for diagnoses of various brain disorders such as epilepsy and tumour. Different approaches are devised to exactly localize the active sources with minimum localization error, less complexity and more validation which include minimum norm, low resolution brain electromagnetic tomography (LORETA), standardized LORETA, exact LORETA, Multiple Signal classifier, focal under determined system solution etc. This paper discusses and compares the ability of localizing the sources for two low resolution methods i.e., sLORETA and eLORETA respectively. The ERP data with visual stimulus is used for comparison at four different time instants for both methods (sLORETA and eLORETA) and then corresponding activation in terms of scalp map, slice view and cortex map is discussed.
    Matched MeSH terms: Electroencephalography/methods*; Electroencephalography/standards
  13. The effects of chronic mitragynine (Kratom) exposure on the EEG in rats
    Suhaimi FW, Hassan Z, Mansor SM, Müller CP
    Neurosci Lett, 2021 02 06;745:135632.
    PMID: 33444671 DOI: 10.1016/j.neulet.2021.135632
    Mitragynine is the main alkaloid isolated from the leaves of Mitragyna speciosa Korth (Kratom). Kratom has been widely used to relieve pain and opioid withdrawal symptoms in humans but may also cause memory deficits. Here we investigated the changes in brain electroencephalogram (EEG) activity after acute and chronic exposure to mitragynine in freely moving rats. Vehicle, morphine (5 mg/kg) or mitragynine (1, 5 and 10 mg/kg) were administered for 28 days, and EEG activity was repeatedly recorded from the frontal cortex, neocortex and hippocampus. Repeated exposure to mitragynine increased delta, but decreased alpha powers in both cortical regions. It further decreased delta power in the hippocampus. These findings suggest that acute and chronic mitragynine can have profound effects on EEG activity, which may underlie effects on behavioral activity and cognition, particularly learning and memory function.
    Matched MeSH terms: Electroencephalography/drug effects*; Electroencephalography/methods
  14. Fulltext An EEG Database and Its Initial Benchmark Emotion Classification Performance
    Seal A, Reddy PPN, Chaithanya P, Meghana A, Jahnavi K, Krejcar O, et al.
    Comput Math Methods Med, 2020;2020:8303465.
    PMID: 32831902 DOI: 10.1155/2020/8303465
    Human emotion recognition has been a major field of research in the last decades owing to its noteworthy academic and industrial applications. However, most of the state-of-the-art methods identified emotions after analyzing facial images. Emotion recognition using electroencephalogram (EEG) signals has got less attention. However, the advantage of using EEG signals is that it can capture real emotion. However, very few EEG signals databases are publicly available for affective computing. In this work, we present a database consisting of EEG signals of 44 volunteers. Twenty-three out of forty-four are females. A 32 channels CLARITY EEG traveler sensor is used to record four emotional states namely, happy, fear, sad, and neutral of subjects by showing 12 videos. So, 3 video files are devoted to each emotion. Participants are mapped with the emotion that they had felt after watching each video. The recorded EEG signals are considered further to classify four types of emotions based on discrete wavelet transform and extreme learning machine (ELM) for reporting the initial benchmark classification performance. The ELM algorithm is used for channel selection followed by subband selection. The proposed method performs the best when features are captured from the gamma subband of the FP1-F7 channel with 94.72% accuracy. The presented database would be available to the researchers for affective recognition applications.
    Matched MeSH terms: Electroencephalography/methods*; Electroencephalography/statistics & numerical data
  15. 48-hour video-EEG monitoring for epilepsy presurgical evaluation is cost-effective and safe in resource-limited setting
    Lim KS, Fong SL, Thuy Le MA, Ahmad Bazir S, Narayanan V, Ismail N, et al.
    Epilepsy Res, 2020 05;162:106298.
    PMID: 32172144 DOI: 10.1016/j.eplepsyres.2020.106298
    INTRODUCTION: Video-EEG monitoring is one of the key investigations in epilepsy pre-surgical evaluation but limited by cost. This study aimed to determine the efficacy and safety of a 48-hour (3-day) video EEG monitoring, with rapid pre-monitoring antiepileptic drugs withdrawal.

    MATERIAL AND METHODS: This is a retrospective study of epilepsy cases with VEM performed in University Malaya Medical Center (UMMC), Kuala Lumpur, from January 2012 till August 2016.

    RESULTS: A total of 137 cases were included. The mean age was 34.5 years old (range 15-62) and 76 (55.8 %) were male. On the first 24 -h of recording (D1), 81 cases (59.1 %) had seizure occurrence, and 109 (79.6 %) by day 2 (D2). One-hundred and nine VEMs (79.6 %) were diagnostic, in guiding surgical decision or further investigations. Of these, 21 had less than 2 seizures recorded in the first 48 h but were considered as diagnostic because of concordant interictal ± ictal activities, or a diagnosis such as psychogenic non-epileptic seizure was made. Twenty-eight patients had extension of VEM for another 24-48 h, and 11 developed seizures during the extension period. Extra-temporal lobe epilepsy and seizure frequency were significant predictors for diagnostic 48 -h VEM. Three patients developed complications, including status epilepticus required anaesthetic agents (1), seizure clusters (2) with postictal psychosis or dysphasia, and all recovered subsequently.

    CONCLUSIONS: 48-h video EEG monitoring is cost-effective in resource limited setting.

    Matched MeSH terms: Electroencephalography/economics; Electroencephalography/methods*
  16. Fulltext Selection of Mother Wavelet Functions for Multi-Channel EEG Signal Analysis during a Working Memory Task
    Al-Qazzaz NK, Bin Mohd Ali SH, Ahmad SA, Islam MS, Escudero J
    Sensors (Basel), 2015;15(11):29015-35.
    PMID: 26593918 DOI: 10.3390/s151129015
    We performed a comparative study to select the efficient mother wavelet (MWT) basis functions that optimally represent the signal characteristics of the electrical activity of the human brain during a working memory (WM) task recorded through electro-encephalography (EEG). Nineteen EEG electrodes were placed on the scalp following the 10-20 system. These electrodes were then grouped into five recording regions corresponding to the scalp area of the cerebral cortex. Sixty-second WM task data were recorded from ten control subjects. Forty-five MWT basis functions from orthogonal families were investigated. These functions included Daubechies (db1-db20), Symlets (sym1-sym20), and Coiflets (coif1-coif5). Using ANOVA, we determined the MWT basis functions with the most significant differences in the ability of the five scalp regions to maximize their cross-correlation with the EEG signals. The best results were obtained using "sym9" across the five scalp regions. Therefore, the most compatible MWT with the EEG signals should be selected to achieve wavelet denoising, decomposition, reconstruction, and sub-band feature extraction. This study provides a reference of the selection of efficient MWT basis functions.
    Matched MeSH terms: Electroencephalography
  17. Idiopathic paroxysmal kinesigenic dyskinesia in Malaysia, a multi-racial Southeast Asian country
    Tai ML, Lim SY, Tan CT
    J Clin Neurosci, 2010 Aug;17(8):1089-90.
    PMID: 20542699 DOI: 10.1016/j.jocn.2009.11.034
    Paroxysmal kinesigenic dyskinesia is a rare disorder, and there are few reports of Asian patients with this condition. We reviewed the clinical features of all patients with idiopathic paroxysmal kinesigenic dyskinesia (PKD) seen at a major neurological centre in Malaysia. The charts of 11 patients with idiopathic PKD seen between 1995 and 2008 were reviewed retrospectively. The male:female ratio was 9:2. Ten patients were of Chinese ethnicity, and one was Malay. Three patients (from two families) had a family history of PKD. The involuntary movement was dystonia in 73% of patients. In one patient, attacks were precipitated by vestibular stimulation. One patient had generalized epilepsy. Another patient who did not have epilepsy demonstrated epileptiform discharges. Only slightly over one-quarter of patients had a positive family history. Males, and people of Chinese ancestry, seem to be affected more frequently by PKD in certain Asian populations.
    Matched MeSH terms: Electroencephalography
  18. Motor imaginary-based brain-machine interface design using programmable logic controllers for the disabled
    Jeyabalan V, Samraj A, Loo CK
    Comput Methods Biomech Biomed Engin, 2010 Oct;13(5):617-23.
    PMID: 20336561 DOI: 10.1080/10255840903405678
    Aiming at the implementation of brain-machine interfaces (BMI) for the aid of disabled people, this paper presents a system design for real-time communication between the BMI and programmable logic controllers (PLCs) to control an electrical actuator that could be used in devices to help the disabled. Motor imaginary signals extracted from the brain’s motor cortex using an electroencephalogram (EEG) were used as a control signal. The EEG signals were pre-processed by means of adaptive recursive band-pass filtrations (ARBF) and classified using simplified fuzzy adaptive resonance theory mapping (ARTMAP) in which the classified signals are then translated into control signals used for machine control via the PLC. A real-time test system was designed using MATLAB for signal processing, KEP-Ware V4 OLE for process control (OPC), a wireless local area network router, an Omron Sysmac CPM1 PLC and a 5 V/0.3A motor. This paper explains the signal processing techniques, the PLC's hardware configuration, OPC configuration and real-time data exchange between MATLAB and PLC using the MATLAB OPC toolbox. The test results indicate that the function of exchanging real-time data can be attained between the BMI and PLC through OPC server and proves that it is an effective and feasible method to be applied to devices such as wheelchairs or electronic equipment.
    Matched MeSH terms: Electroencephalography
  19. Two dimensional affective state distribution of the brain under emotion stimuli
    Yaacob H, Karim I, Wahab A, Kamaruddin N
    Annu Int Conf IEEE Eng Med Biol Soc, 2012;2012:6052-5.
    PMID: 23367309 DOI: 10.1109/EMBC.2012.6347374
    Emotions are ambiguous. Many techniques have been employed to perform emotion prediction and to understand emotional elicitations. Brain signals measured using electroencephalogram (EEG) are also used in studies about emotions. Using KDE as feature extraction technique and MLP for performing supervised learning on the brain signals. It has shown that all channels in EEG can capture emotional experience. In addition it was also indicated that emotions are dynamic as represented by the level of valence and the intensity of arousal. Such findings are useful in biomedical studies, especially in dealing with emotional disorders which can results in using a two-channel EEG device for neurofeedback applications.
    Matched MeSH terms: Electroencephalography
  20. Brain functional connectivity patterns for emotional state classification in Parkinson's disease patients without dementia
    Yuvaraj R, Murugappan M, Acharya UR, Adeli H, Ibrahim NM, Mesquita E
    Behav Brain Res, 2016 Feb 1;298(Pt B):248-60.
    PMID: 26515932 DOI: 10.1016/j.bbr.2015.10.036
    Successful emotional communication is crucial for social interactions and social relationships. Parkinson's Disease (PD) patients have shown deficits in emotional recognition abilities although the research findings are inconclusive. This paper presents an investigation of six emotions (happiness, sadness, fear, anger, surprise, and disgust) of twenty non-demented (Mini-Mental State Examination score >24) PD patients and twenty Healthy Controls (HCs) using Electroencephalogram (EEG)-based Brain Functional Connectivity (BFC) patterns. The functional connectivity index feature in EEG signals is computed using three different methods: Correlation (COR), Coherence (COH), and Phase Synchronization Index (PSI). Further, a new functional connectivity index feature is proposed using bispectral analysis. The experimental results indicate that the BFC change is significantly different among emotional states of PD patients compared with HC. Also, the emotional connectivity pattern classified using Support Vector Machine (SVM) classifier yielded the highest accuracy for the new bispectral functional connectivity index. The PD patients showed emotional impairments as demonstrated by a poor classification performance. This finding suggests that decrease in the functional connectivity indices during emotional stimulation in PD, indicating functional disconnections between cortical areas.
    Matched MeSH terms: Electroencephalography
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