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Visual brain activity patterns classification with simultaneous EEG-fMRI: A multimodal approach

Ahmad RF, Malik AS, Kamel N, Reza F, Amin HU, Hussain M

Technol Health Care, 2017;25(3):471-485.
PMID: 27935575 DOI: 10.3233/THC-161286

BACKGROUND: Classification of the visual information from the brain activity data is a challenging task. Many studies reported in the literature are based on the brain activity patterns using either fMRI or EEG/MEG only. EEG and fMRI considered as two complementary neuroimaging modalities in terms of their temporal and spatial resolution to map the brain activity. For getting a high spatial and temporal resolution of the brain at the same time, simultaneous EEG-fMRI seems to be fruitful.
METHODS: In this article, we propose a new method based on simultaneous EEG-fMRI data and machine learning approach to classify the visual brain activity patterns. We acquired EEG-fMRI data simultaneously on the ten healthy human participants by showing them visual stimuli. Data fusion approach is used to merge EEG and fMRI data. Machine learning classifier is used for the classification purposes.
RESULTS: Results showed that superior classification performance has been achieved with simultaneous EEG-fMRI data as compared to the EEG and fMRI data standalone. This shows that multimodal approach improved the classification accuracy results as compared with other approaches reported in the literature.
CONCLUSIONS: The proposed simultaneous EEG-fMRI approach for classifying the brain activity patterns can be helpful to predict or fully decode the brain activity patterns.

Matched MeSH terms: Brain Waves/physiology
Inter-hemispheric EEG coherence analysis in Parkinson's disease: assessing brain activity during emotion processing

Yuvaraj R, Murugappan M, Ibrahim NM, Sundaraj K, Omar MI, Mohamad K, et al.

J Neural Transm (Vienna), 2015 Feb;122(2):237-52.
PMID: 24894699 DOI: 10.1007/s00702-014-1249-4

Parkinson's disease (PD) is not only characterized by its prominent motor symptoms but also associated with disturbances in cognitive and emotional functioning. The objective of the present study was to investigate the influence of emotion processing on inter-hemispheric electroencephalography (EEG) coherence in PD. Multimodal emotional stimuli (happiness, sadness, fear, anger, surprise, and disgust) were presented to 20 PD patients and 30 age-, education level-, and gender-matched healthy controls (HC) while EEG was recorded. Inter-hemispheric coherence was computed from seven homologous EEG electrode pairs (AF3-AF4, F7-F8, F3-F4, FC5-FC6, T7-T8, P7-P8, and O1-O2) for delta, theta, alpha, beta, and gamma frequency bands. In addition, subjective ratings were obtained for a representative of emotional stimuli. Interhemispherically, PD patients showed significantly lower coherence in theta, alpha, beta, and gamma frequency bands than HC during emotion processing. No significant changes were found in the delta frequency band coherence. We also found that PD patients were more impaired in recognizing negative emotions (sadness, fear, anger, and disgust) than relatively positive emotions (happiness and surprise). Behaviorally, PD patients did not show impairment in emotion recognition as measured by subjective ratings. These findings suggest that PD patients may have an impairment of inter-hemispheric functional connectivity (i.e., a decline in cortical connectivity) during emotion processing. This study may increase the awareness of EEG emotional response studies in clinical practice to uncover potential neurophysiologic abnormalities.

Matched MeSH terms: Brain Waves/physiology*
The relation between upper limb muscle and brain activity in two precision levels of repetitive light tasks

Zadry HR, Dawal SZ, Taha Z

Int J Occup Saf Ergon, 2011;17(4):373-84.
PMID: 22152503

A study was conducted to investigate the effects of repetitive light tasks of low and high precision on upper limb muscles and brain activities. Surface electromyography (EMG) and electroencephalography (EEG) were used to measure the muscle and brain activity of 10 subjects. The results show that the root-mean-square (RMS) and mean power frquency (MPF) of the muscle activity and the mean power of the EEG alpha bands were higher on the high-precision task than on the low-precision one. There was also a high and significant correlation between upper limb muscle and brain activity during the tasks. The longer the time and the more precise the task, the more the subjects become fatigued both physically and mentally. Thus, these results could be potentially useful in managing fatigue, especially fatique related to muscle and mental workload.

Matched MeSH terms: Brain Waves/physiology*
Development of statistical models for predicting muscle and mental activities during repetitive precision tasks

Zadry HR, Dawal SZ, Taha Z

Int J Occup Saf Ergon, 2016 Sep;22(3):374-83.
PMID: 27053140 DOI: 10.1080/10803548.2016.1150094

This study was conducted to develop muscle and mental activities on repetitive precision tasks. A laboratory experiment was used to address the objectives. Surface electromyography was used to measure muscle activities from eight upper limb muscles, while electroencephalography recorded mental activities from six channels. Fourteen university students participated in the study. The results show that muscle and mental activities increase for all tasks, indicating the occurrence of muscle and mental fatigue. A linear relationship between muscle activity, mental activity and time was found while subjects were performing the task. Thus, models were developed using those variables. The models were found valid after validation using other students' and workers' data. Findings from this study can contribute as a reference for future studies investigating muscle and mental activity and can be applied in industry as guidelines to manage muscle and mental fatigue, especially to manage job schedules and rotation.

Matched MeSH terms: Brain Waves/physiology*
Incremental benefits of EEG informed fMRI in the study of disorders related to meso-corticolimbic dopamine pathway dysfunction: A systematic review of recent literature

Syed Nasser N, Ibrahim B, Sharifat H, Abdul Rashid A, Suppiah S

J Clin Neurosci, 2019 Jul;65:87-99.
PMID: 30955950 DOI: 10.1016/j.jocn.2019.03.054

Functional magnetic resonance imaging (fMRI) is a non-invasive imaging modality that enables the assessment of neural connectivity and oxygen utility of the brain using blood oxygen level dependent (BOLD) imaging sequence. Electroencephalography (EEG), on the other hands, looks at cortical electrical impulses of the brain thus detecting brainwave patterns during rest and thought processing. The combination of these two modalities is called fMRI with simultaneous EEG (fMRI-EEG), which has emerged as a new tool for experimental neuroscience assessments and has been applied clinically in many settings, most commonly in epilepsy cases. Recent advances in imaging has led to fMRI-EEG being utilized in behavioural studies which can help in giving an objective assessment of ambiguous cases and help in the assessment of response to treatment by providing a non-invasive biomarker of the disease processes. We aim to review the role and interpretation of fMRI-EEG in studies pertaining to psychiatric disorders and behavioral abnormalities.

Matched MeSH terms: Brain Waves/physiology
Fulltext A System for True and False Memory Prediction Based on 2D and 3D Educational Contents and EEG Brain Signals

Bamatraf S, Hussain M, Aboalsamh H, Qazi EU, Malik AS, Amin HU, et al.

Comput Intell Neurosci, 2016;2016:8491046.
PMID: 26819593 DOI: 10.1155/2016/8491046

We studied the impact of 2D and 3D educational contents on learning and memory recall using electroencephalography (EEG) brain signals. For this purpose, we adopted a classification approach that predicts true and false memories in case of both short term memory (STM) and long term memory (LTM) and helps to decide whether there is a difference between the impact of 2D and 3D educational contents. In this approach, EEG brain signals are converted into topomaps and then discriminative features are extracted from them and finally support vector machine (SVM) which is employed to predict brain states. For data collection, half of sixty-eight healthy individuals watched the learning material in 2D format whereas the rest watched the same material in 3D format. After learning task, memory recall tasks were performed after 30 minutes (STM) and two months (LTM), and EEG signals were recorded. In case of STM, 97.5% prediction accuracy was achieved for 3D and 96.6% for 2D and, in case of LTM, it was 100% for both 2D and 3D. The statistical analysis of the results suggested that for learning and memory recall both 2D and 3D materials do not have much difference in case of STM and LTM.

Matched MeSH terms: Brain Waves/physiology*
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: Brain Waves/physiology
Clinical, semiological, electroencephalographic, and neuropsychological features of "pure" neocortical temporal lobe epilepsy

Maizuliana H, Usui N, Terada K, Kondo A, Inoue Y

Epileptic Disord, 2020 Feb 01;22(1):55-65.
PMID: 32031536 DOI: 10.1684/epd.2020.1132

We examined the clinical, semiological, scalp EEG, and neuropsychological features of patients with "pure" neocortical temporal lobe epilepsy (NTLE) who were successfully treated by neocortical temporal resection sparing the mesial temporal structures. This retrospective study included 17 patients with lesional NTLE who satisfied the following criteria: presence of a discrete structural lesion in the lateral temporal lobe on preoperative MRI; lateral temporal resection sparing the mesial temporal structures; follow-up for at least two years after surgery; and favourable postoperative seizure outcome (Engel Class I). The study included 10 females and seven males, and the age at surgery ranged from 15 to 48 years (mean: 30.7 years). Auras, video-recorded seizure semiology, interictal and ictal EEG, and pre- and post-operative neuropsychological data were reviewed. Twenty patients with mesial temporal lobe epilepsy (MTLE) with hippocampal sclerosis, who had a favourable postoperative seizure outcome (Engel Class I), were selected as a control group. Age at seizure onset was significantly greater in patients with NTLE than in controls. A history of febrile convulsion was significantly less frequent in NTLE patients. Epigastric ascending sensation (6% versus 40%; p=0.017), oral automatisms (29% versus 80%; p=0.003), gestural automatisms (47% versus 80%; p=0.047), and dystonic posturing (0% versus 40%; p=0.003) were significantly less frequent in NTLE than controls. Ictal unitemporal rhythmic theta activity was also significantly less frequent in NTLE than controls (35.3% versus 75%; p=0.015). Preoperative IQ score (range: 68 to 114; mean: 88.9) and preoperative memory quotient score (range: 56-122; mean: 98.1) were significantly higher in NTLE (p=0.003 and p=0.048, respectively). There were notable differences in clinical, semiological, EEG, and neuropsychological features between "pure" NTLE and MTLE. These findings may be useful to identify the epileptogenic zone.

Matched MeSH terms: Brain Waves/physiology*