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Fulltext Anticonvulsant activity of α-terpineol isolated from myristica fragrans in GAERS model of absence epilepsy

Islam, M.R., Muthuraju, S., Tarmizi, C.H., Zulkifli, M.M., Osman, H., Mohamad, H., et al.

ASM Science Journal, 2012;6(2):95-102.
MyJurnal

Epilepsy is a neurological disorder characterized by recurrent seizures resulting from excessive abnormal electrical discharges in the brain. Medicinal plants may play an invaluable role to discover the new antiepileptic drugs. The aim of the present study was to investigate the anticonvulsant activity of α-terpineol isolated from Myristica fragrans Hountt. The α-terpineol showed a significant inhibition of the seizure episodes and spikes in absence seizures model of Genetic Absence Epilepsy Rats from Strasbourg (GAERS) rats by using electroencephalography records. It showed dose-dependent anticonvulsant activity that was comparable to the known antiepileptic drug of diazepam. It showed a rapid onset and relatively short duration of anticonvulsant effects. The present findings suggest that α-terpineol might possess antiepileptic activities against the partial seizures of human because it prevented seizures in well-established genetic absence seizure animal model of GAERS rats.

Matched MeSH terms: Electroencephalography
Fulltext Conversion Symptoms In Schizophrenia: A Case Report

Ting, J.H., Nor Zuraida, Z., Sharmilla, K., Salina, M.

Malaysian Journal of Psychiatry, 2009;18(2):1-5.
MyJurnal

We report a 35 year-old Iranian female who presented with a sudden onset of left sided hemiparesis associated with temporary loss of consciousness of about 3 minutes. Neurological examination revealed a power of 0/5 over the left upper/lower limbs but reflexes were normal and plantar reflex was downgoing and equivocal. A computed tomography scan was done and it revealed mild bilateral frontal atrophy and a temporal arachnoid cyst which was decidedly an incidental finding and it did not have any relation to the clinical presentation. Electroencephalogram and other laboratory findings were all normal. When the psychiatric team interviewed her, it was revealed that the patient had recently experienced a major stressful event just prior to the hemiparesis. On further interview, the patient had complained of delusions of persecution, delusions of reference and also auditory hallucinations for approximately a year prior to admission. There have been only a spatter of reports of conversion symptoms seen in patients with schizophrenia and this is such a case.

Matched MeSH terms: Electroencephalography
Fulltext Level 4 comprehensive epilepsy program in Malaysia, a resource-limited country

Lim, Kheng-Seang, Sherrini Ahmad Bazir Ahmad, Vairavan Narayanan, Kartini Rahmat, Norlisah Mohd Ramli, Mun, Kein-Seong, et al.

Neurology Asia, 2017;22(4):299-305.
MyJurnal

Background and Objective: There is a great challenge to establish a level 4 epilepsy care offering
complete evaluation for epilepsy surgery including invasive monitoring in a resource-limited country.
This study aimed to report the setup of a level 4 comprehensive epilepsy program in Malaysia and the
outcome of epilepsy surgery over the past 4 years.

Methods: This is a retrospective study analyzing
cases with intractable epilepsy in a comprehensive epilepsy program in University Malaya Medical
Center (UMMC), Kuala Lumpur, from January 2012 to August 2016.

Results: A total of 92 cases
had comprehensive epilepsy evaluation from January 2012 till August 2016. The mean age was 35.57
years old (range 15-59) and 54 (58.7%) were male. There were 17 cases having epilepsy surgery
after stage-1 evaluation. Eleven cases had mesial temporal sclerosis and 81% achieved Engel class
I surgical outcome. Six cases had lesionectomy and 60% had Engel class I outcome. A total of 16
surgeries were performed after stage-2 evaluation, including invasive EEG monitoring in 9 cases.
Among those with surgery performed more than 12 months from the time of data collection, 5/10
(50%) achieved Engel I outcome, whereas 2 (20%) had worthwhile improvement (Engel class III)
with 75% and 90% seizure reduction.

Conclusion: Level 4 epilepsy care has an important role and is possible with joint multidisciplinary
effort in a middle-income country like Malaysia despite resource limitation.

Matched MeSH terms: Electroencephalography
Fulltext Classification of Visual and Non-visual Learners Using Electroencephalographic Alpha and Gamma Activities

Jawed S, Amin HU, Malik AS, Faye I

Front Behav Neurosci, 2019;13:86.
PMID: 31133829 DOI: 10.3389/fnbeh.2019.00086

This study analyzes the learning styles of subjects based on their electroencephalo-graphy (EEG) signals. The goal is to identify how the EEG features of a visual learner differ from those of a non-visual learner. The idea is to measure the students' EEGs during the resting states (eyes open and eyes closed conditions) and when performing learning tasks. For this purpose, 34 healthy subjects are recruited. The subjects have no background knowledge of the animated learning content. The subjects are shown the animated learning content in a video format. The experiment consists of two sessions and each session comprises two parts: (1) Learning task: the subjects are shown the animated learning content for an 8-10 min duration. (2) Memory retrieval task The EEG signals are measured during the leaning task and memory retrieval task in two sessions. The retention time for the first session was 30 min, and 2 months for the second session. The analysis is performed for the EEG measured during the memory retrieval tasks. The study characterizes and differentiates the visual learners from the non-visual learners considering the extracted EEG features, such as the power spectral density (PSD), power spectral entropy (PSE), and discrete wavelet transform (DWT). The PSD and DWT features are analyzed. The EEG PSD and DWT features are computed for the recorded EEG in the alpha and gamma frequency bands over 128 scalp sites. The alpha and gamma frequency band for frontal, occipital, and parietal regions are analyzed as these regions are activated during learning. The extracted PSD and DWT features are then reduced to 8 and 15 optimum features using principal component analysis (PCA). The optimum features are then used as an input to the k-nearest neighbor (k-NN) classifier using the Mahalanobis distance metric, with 10-fold cross validation and support vector machine (SVM) classifier using linear kernel, with 10-fold cross validation. The classification results showed 97% and 94% accuracies rate for the first session and 96% and 93% accuracies for the second session in the alpha and gamma bands for the visual learners and non-visual learners, respectively, for k-NN classifier for PSD features and 68% and 100% accuracies rate for first session and 100% accuracies rate for second session for DWT features using k-NN classifier for the second session in the alpha and gamma band. For PSD features 97% and 96% accuracies rate for the first session, 100% and 95% accuracies rate for second session using SVM classifier and 79% and 82% accuracy for first session and 56% and 74% accuracy for second session for DWT features using SVM classifier. The results showed that the PSDs in the alpha and gamma bands represent distinct and stable EEG signatures for visual learners and non-visual learners during the retrieval of the learned contents.

Matched MeSH terms: Electroencephalography
Fulltext A Deep Learning Model for Automated Sleep Stages Classification Using PSG Signals

Yildirim O, Baloglu UB, Acharya UR

Int J Environ Res Public Health, 2019 02 19;16(4).
PMID: 30791379 DOI: 10.3390/ijerph16040599

Sleep disorder is a symptom of many neurological diseases that may significantly affect the quality of daily life. Traditional methods are time-consuming and involve the manual scoring of polysomnogram (PSG) signals obtained in a laboratory environment. However, the automated monitoring of sleep stages can help detect neurological disorders accurately as well. In this study, a flexible deep learning model is proposed using raw PSG signals. A one-dimensional convolutional neural network (1D-CNN) is developed using electroencephalogram (EEG) and electrooculogram (EOG) signals for the classification of sleep stages. The performance of the system is evaluated using two public databases (sleep-edf and sleep-edfx). The developed model yielded the highest accuracies of 98.06%, 94.64%, 92.36%, 91.22%, and 91.00% for two to six sleep classes, respectively, using the sleep-edf database. Further, the proposed model obtained the highest accuracies of 97.62%, 94.34%, 92.33%, 90.98%, and 89.54%, respectively for the same two to six sleep classes using the sleep-edfx dataset. The developed deep learning model is ready for clinical usage, and can be tested with big PSG data.

Matched MeSH terms: Electroencephalography
A review of automated sleep stage scoring based on physiological signals for the new millennia

Faust O, Razaghi H, Barika R, Ciaccio EJ, Acharya UR

Comput Methods Programs Biomed, 2019 Jul;176:81-91.
PMID: 31200914 DOI: 10.1016/j.cmpb.2019.04.032

BACKGROUND AND OBJECTIVE: Sleep is an important part of our life. That importance is highlighted by the multitude of health problems which result from sleep disorders. Detecting these sleep disorders requires an accurate interpretation of physiological signals. Prerequisite for this interpretation is an understanding of the way in which sleep stage changes manifest themselves in the signal waveform. With that understanding it is possible to build automated sleep stage scoring systems. Apart from their practical relevance for automating sleep disorder diagnosis, these systems provide a good indication of the amount of sleep stage related information communicated by a specific physiological signal.
METHODS: This article provides a comprehensive review of automated sleep stage scoring systems, which were created since the year 2000. The systems were developed for Electrocardiogram (ECG), Electroencephalogram (EEG), Electrooculogram (EOG), and a combination of signals.
RESULTS: Our review shows that all of these signals contain information for sleep stage scoring.
CONCLUSIONS: The result is important, because it allows us to shift our research focus away from information extraction methods to systemic improvements, such as patient comfort, redundancy, safety and cost.

Matched MeSH terms: Electroencephalography
18F-FDG PET/CT for the pre-surgical localization of epileptogenic focus among paediatric patients with drug resistant epilepsy in Malaysia: perspective of a nuclear medicine physician

Abdul Aziz AF, Mohamed AR, Murugesu S, Siti Zarina AH, Lee BN

Med J Malaysia, 2021 07;76(4):502-509.
PMID: 34305111

BACKGROUND: Scalp video electroencephalography monitoring (VEM) and brain MRI sometime fail to identify the epileptogenic focus (EF) in patients with drug resistant epilepsy (DRE). 18F-FDG PET/CT has been shown to improve the detection of EF in patients but is not widely used in Malaysia. Thus, the objective of this study was to identify whether 18F-FDG PET/CT conferred an added benefit in the pre-surgical evaluation of DRE.
METHODS: Retrospective review of 119 consecutive paediatric patients referred for 18F-FDG-PET/CT at the Department of Nuclear Medicine of the National Cancer Institute, Putrajaya. All had DRE and underwent evaluation at the Paediatric Institute, Hospital Kuala Lumpur. Visually detected areas of 18F-FDG-PET/CT hypometabolism were correlated with clinical, MRI and VEM findings.
RESULTS: Hypometabolism was detected in 102/119 (86%) 18FFDG- PET/CT scans. The pattern of hypometabolism in 73 patients with normal MRI was focal unilobar in 16/73 (22%), multilobar unilateral in 8/73 (11%), bilateral in 27/73 (37%) and global in 5/73 (7%) of patients; whilst 17/73 (23%) showed normal metabolism. In 46 patients with lesions on MRI, 18F-FDG-PET/CT showed concordant localisation and lateralization of the EF in 30/46 (65%) patients, and bilateral or widespread hypometabolism in the rest. Addition of 18FFDG PET/CT impacted decision making in 66/119 (55%) of patients; 24/73 with non-lesional and 30/46 patients with lesional epilepsies were recommended for surgery or further surgical work up, whilst surgery was not recommended in 11/46 patients with lesional epilepsy due to bilateral or widespread hypometabolism. 25 patients subsequently underwent epilepsy surgery, with 16/25 becoming seizure free following surgery.
CONCLUSION: 18F-FDG-PET/CT has an added benefit for the localization and lateralization of EF, particularly in patients with normal or inconclusive MRI.

Matched MeSH terms: Electroencephalography
Fulltext More than a drug target: Purinergic signalling as a source for diagnostic tools in epilepsy

Wong ZW, Engel T

Neuropharmacology, 2023 Jan 01;222:109303.
PMID: 36309046 DOI: 10.1016/j.neuropharm.2022.109303

Epilepsy is one of the most common and disabling chronic neurological diseases affecting people of all ages. Major challenges of epilepsy management include the persistently high percentage of drug-refractoriness among patients, the absence of disease-modifying treatments, and its diagnosis and prognosis. To date, long-term video-electroencephalogram (EEG) recordings remain the gold standard for an epilepsy diagnosis. However, this is very costly, has low throughput, and in some instances has very limited availability. Therefore, much effort is put into the search for non-invasive diagnostic tests. Purinergic signalling, via extracellularly released adenosine triphosphate (ATP), is gaining increasing traction as a therapeutic strategy for epilepsy treatment which is supported by evidence from both experimental models and patients. This includes in particular the ionotropic P2X7 receptor. Besides that, other components from the ATPergic signalling cascade such as the metabotropic P2Y receptors (e.g., P2Y1 receptor) and ATP-release channels (e.g., pannexin-1), have also been shown to contribute to seizures and epilepsy. In addition to the therapeutic potential of purinergic signalling, emerging evidence has also shown its potential as a diagnostic tool. Following seizures and epilepsy, the concentration of purines in the blood and the expression of different compounds of the purinergic signalling cascade are significantly altered. Herein, this review will provide a detailed discussion of recent findings on the diagnostic potential of purinergic signalling for epilepsy management and the prospect of translating it for clinical application. This article is part of the Special Issue on 'Purinergic Signaling: 50 years'.

Matched MeSH terms: Electroencephalography
Fulltext Optimizing Residual Networks and VGG for Classification of EEG Signals: Identifying Ideal Channels for Emotion Recognition

Cheah KH, Nisar H, Yap VV, Lee CY, Sinha GR

J Healthc Eng, 2021;2021:5599615.
PMID: 33859808 DOI: 10.1155/2021/5599615

Emotion is a crucial aspect of human health, and emotion recognition systems serve important roles in the development of neurofeedback applications. Most of the emotion recognition methods proposed in previous research take predefined EEG features as input to the classification algorithms. This paper investigates the less studied method of using plain EEG signals as the classifier input, with the residual networks (ResNet) as the classifier of interest. ResNet having excelled in the automated hierarchical feature extraction in raw data domains with vast number of samples (e.g., image processing) is potentially promising in the future as the amount of publicly available EEG databases has been increasing. Architecture of the original ResNet designed for image processing is restructured for optimal performance on EEG signals. The arrangement of convolutional kernel dimension is demonstrated to largely affect the model's performance on EEG signal processing. The study is conducted on the Shanghai Jiao Tong University Emotion EEG Dataset (SEED), with our proposed ResNet18 architecture achieving 93.42% accuracy on the 3-class emotion classification, compared to the original ResNet18 at 87.06% accuracy. Our proposed ResNet18 architecture has also achieved a model parameter reduction of 52.22% from the original ResNet18. We have also compared the importance of different subsets of EEG channels from a total of 62 channels for emotion recognition. The channels placed near the anterior pole of the temporal lobes appeared to be most emotionally relevant. This agrees with the location of emotion-processing brain structures like the insular cortex and amygdala.

Matched MeSH terms: Electroencephalography
Fulltext Local context influences memory for emotional stimuli but not electrophysiological markers of emotion-dependent attention

Barnacle GE, Tsivilis D, Schaefer A, Talmi D

Psychophysiology, 2018 04;55(4).
PMID: 29023754 DOI: 10.1111/psyp.13014

Emotional enhancement of free recall can be context dependent. It is readily observed when emotional and neutral scenes are encoded and recalled together in a "mixed" list, but diminishes when these scenes are encoded separately in "pure" lists. We examined the hypothesis that this effect is due to differences in allocation of attention to neutral stimuli according to whether they are presented in mixed or pure lists, especially when encoding is intentional. Using picture stimuli that were controlled for semantic relatedness, our results contradicted this hypothesis. The amplitude of well-known electrophysiological markers of emotion-related attention-the early posterior negativity (EPN), the late positive potential (LPP), and the slow wave (SW)-was higher for emotional stimuli. Crucially, the emotional modulation of these ERPs was insensitive to list context, observed equally in pure and mixed lists. Although list context did not modulate neural markers of emotion-related attention, list context did modulate the effect of emotion on free recall. The apparent decoupling of the emotional effects on attention and memory, challenges existing hypotheses accounting for the emotional enhancement of memory. We close by discussing whether findings are more compatible with an alternative hypothesis, where the magnitude of emotional memory enhancement is, at least in part, a consequence of retrieval dynamics.

Matched MeSH terms: Electroencephalography
Fulltext Big Brain Data Initiative in Universiti Sains Malaysia: Challenges in Brain Mapping for Malaysia

Shekh Ibrahim SA, Hamzah N, Abdul Wahab AR, Abdullah JM, Nurul Hashimah Ahamed Hassain Malim, Sumari P, et al.

Malays J Med Sci, 2020 Jul;27(4):1-8.
PMID: 32863741 DOI: 10.21315/mjms2020.27.4.1

Universiti Sains Malaysia has started the Big Brain Data Initiative project since the last two years as brain mapping techniques have proven to be important in understanding the molecular, cellular and functional mechanisms of the brain. This Big Brain Data Initiative can be a platform for neurophysicians and neurosurgeons, psychiatrists, psychologists, cognitive neuroscientists, neurotechnologists and other researchers to improve brain mapping techniques. Data collection from a cohort of multiracial population in Malaysia is important for present and future research and finding cure for neurological and mental illness. Malaysia is one of the participant of the Global Brain Consortium (GBC) supported by the World Health Organization. This project is a part of its contribution via the third GBC goal which is influencing the policy process within and between high-income countries and low- and middle-income countries, such as pathways for fair data-sharing of multi-modal imaging data, starting with electroencephalographic data.

Matched MeSH terms: Electroencephalography
Automation of anaesthesia: a review on multivariable control

Chang JJ, Syafiie S, Kamil R, Lim TA

J Clin Monit Comput, 2015 Apr;29(2):231-9.
PMID: 24961365 DOI: 10.1007/s10877-014-9590-6

Anaesthesia is a multivariable problem where a combination of drugs are used to induce desired hypnotic, analgesia and immobility states. The automation of anaesthesia may improve the safety and cost-effectiveness of anaesthesia. However, the realization of a safe and reliable multivariable closed-loop control of anaesthesia is yet to be achieved due to a manifold of challenges. In this paper, several significant challenges in automation of anaesthesia are discussed, namely model uncertainty, controlled variables, closed-loop application and dependability. The increasingly reliable measurement device, robust and adaptive controller, and better fault tolerance strategy are paving the way for automation of anaesthesia.

Matched MeSH terms: Electroencephalography/drug effects
Effect of mindfulness meditation on brain-computer interface performance

Tan LF, Dienes Z, Jansari A, Goh SY

Conscious Cogn, 2014 Jan;23:12-21.
PMID: 24275085 DOI: 10.1016/j.concog.2013.10.010

Electroencephalogram based brain-computer interfaces (BCIs) enable stroke and motor neuron disease patients to communicate and control devices. Mindfulness meditation has been claimed to enhance metacognitive regulation. The current study explores whether mindfulness meditation training can thus improve the performance of BCI users. To eliminate the possibility of expectation of improvement influencing the results, we introduced a music training condition. A norming study found that both meditation and music interventions elicited clear expectations for improvement on the BCI task, with the strength of expectation being closely matched. In the main 12 week intervention study, seventy-six healthy volunteers were randomly assigned to three groups: a meditation training group; a music training group; and a no treatment control group. The mindfulness meditation training group obtained a significantly higher BCI accuracy compared to both the music training and no-treatment control groups after the intervention, indicating effects of meditation above and beyond expectancy effects.

Matched MeSH terms: Electroencephalography/methods
Drowsiness detection during different times of day using multiple features

Sahayadhas A, Sundaraj K, Murugappan M

Australas Phys Eng Sci Med, 2013 Jun;36(2):243-50.
PMID: 23719977 DOI: 10.1007/s13246-013-0200-6

Driver drowsiness has been one of the major causes of road accidents that lead to severe trauma, such as physical injury, death, and economic loss, which highlights the need to develop a system that can alert drivers of their drowsy state prior to accidents. Researchers have therefore attempted to develop systems that can determine driver drowsiness using the following four measures: (1) subjective ratings from drivers, (2) vehicle-based measures, (3) behavioral measures and (4) physiological measures. In this study, we analyzed the various factors that contribute towards drowsiness. A total of 15 male subjects were asked to drive for 2 h at three different times of the day (00:00-02:00, 03:00-05:00 and 15:00-17:00 h) when the circadian rhythm is low. The less intrusive physiological signal measurements, ECG and EMG, are analyzed during this driving task. Statistically significant differences in the features of ECG and sEMG signals were observed between the alert and drowsy states of the drivers during different times of day. In the future, these physiological measures can be fused with vision-based measures for the development of an efficient drowsiness detection system.

Matched MeSH terms: Electroencephalography/methods*
Neural network classification of autoregressive features from electroencephalogram signals for brain-computer interface design

Huan NJ, Palaniappan R

J Neural Eng, 2004 Sep;1(3):142-50.
PMID: 15876633

In this paper, we have designed a two-state brain-computer interface (BCI) using neural network (NN) classification of autoregressive (AR) features from electroencephalogram (EEG) signals extracted during mental tasks. The main purpose of the study is to use Keirn and Aunon's data to investigate the performance of different mental task combinations and different AR features for BCI design for individual subjects. In the experimental study, EEG signals from five mental tasks were recorded from four subjects. Different combinations of two mental tasks were studied for each subject. Six different feature extraction methods were used to extract the features from the EEG signals: AR coefficients computed with Burg's algorithm, AR coefficients computed with a least-squares (LS) algorithm and adaptive autoregressive (AAR) coefficients computed with a least-mean-square (LMS) algorithm. All the methods used order six applied to 125 data points and these three methods were repeated with the same data but with segmentation into five segments in increments of 25 data points. The multilayer perceptron NN trained by the back-propagation algorithm (MLP-BP) and linear discriminant analysis (LDA) were used to classify the computed features into different categories that represent the mental tasks. We compared the classification performances among the six different feature extraction methods. The results showed that sixth-order AR coefficients with the LS algorithm without segmentation gave the best performance (93.10%) using MLP-BP and (97.00%) using LDA. The results also showed that the segmentation and AAR methods are not suitable for this set of EEG signals. We conclude that, for different subjects, the best mental task combinations are different and proper selection of mental tasks and feature extraction methods are essential for the BCI design.

Matched MeSH terms: Electroencephalography/methods*
Ictal EEG Source Imaging for Presurgical Evaluation of Refractory Focal Epilepsy

Habib MA, Ibrahim F, Mohktar MS, Kamaruzzaman SB, Rahmat K, Lim KS

World Neurosurg, 2016 Apr;88:576-585.
PMID: 26548833 DOI: 10.1016/j.wneu.2015.10.096

BACKGROUND: Electroencephalography source imaging (ESI) is a promising tool for localizing the cortical sources of both ictal and interictal epileptic activities. Many studies have shown the clinical usefulness of interictal ESI, but very few have investigated the utility of ictal ESI. The aim of this article is to examine the clinical usefulness of ictal ESI for epileptic focus localization in patients with refractory focal epilepsy, especially extratemporal lobe epilepsy.
METHODS: Both ictal and interictal ESI were performed by the use of patient-specific realistic forward models and 3 different linear distributed inverse models. Lateralization as well as concordance between ESI-estimated focuses and single-photon emission computed tomography (SPECT) focuses were assessed.
RESULTS: All the ESI focuses (both ictal and interictal) were found lateralized to the same hemisphere as ictal SPECT focuses. Lateralization results also were in agreement with the lesion sides as visualized on magnetic resonance imaging. Ictal ESI results, obtained from the best-performing inverse model, were fully concordant with the same cortical lobe as SPECT focuses, whereas the corresponding concordance rate is 87.50% in case of interictal ESI.
CONCLUSIONS: Our findings show that ictal ESI gives fully lateralized and highly concordant results with ictal SPECT and may provide a cost-effective substitute for ictal SPECT.

Matched MeSH terms: Electroencephalography/methods*
Video-EEG telemetry: apparent manifestation of both epileptic and non-epileptic attacks causing potential diagnostic pitfalls

Raymond AA, Gilmore WV, Scott CA, Fish DR, Smith SJ

Epileptic Disord, 1999 Jun;1(2):101-6.
PMID: 10937139

Video-EEG telemetry is often used to support the diagnosis of non-epileptic seizures (NES). Although rare, some patients may have both epileptic seizures (ES) and NES. It is crucially important to identify such patients to avoid the hazards of inappropriate anticonvulsant withdrawal. To delineate the electroclinical characteristics and diagnostic problems in this group of patients, we studied the clinical, EEG and MRI features of 14 consecutive patients in whom separate attacks, considered to be both NES and ES were recorded using video-EEG telemetry. Only two patients were drug-reduced during the telemetry. Most patients had their first seizure (ES or NES) in childhood (median age 7 years; range: 6 months-24 years); 8/14 patients were female. Brain MRI was abnormal in 10/14 patients. Interictal EEG abnormalities were present in all patients; 13/14 had epileptiform and 1/14 only background abnormalities. Over 70 seizures were recorded in these 14 patients: in 12/14 patients, the first recorded seizure was a NES (p < 0.001), and 7 of these patients had at least one more NES before an ES was recorded. Only 3/14 patients had more than 5 NES before an ES was recorded. Recording a small number of apparently NES in an individual by no means precludes the possibility of additional epilepsy. Particular care should be taken, and multiple (> 5) seizure recording may be advisable, in patients with a young age of seizure onset, interictal EEG abnormalities, or a clear, potential aetiology for epilepsy.

Matched MeSH terms: Electroencephalography*
A new brain-computer interface design using fuzzy ARTMAP

Palaniappan R, Paramesran R, Nishida S, Saiwaki N

IEEE Trans Neural Syst Rehabil Eng, 2002 Sep;10(3):140-8.
PMID: 12503778

This paper proposes a new brain-computer interface (BCI) design using fuzzy ARTMAP (FA) neural network, as well as an application of the design. The objective of this BCI-FA design is to classify the best three of the five available mental tasks for each subject using power spectral density (PSD) values of electroencephalogram (EEG) signals. These PSD values are extracted using the Wiener-Khinchine and autoregressive methods. Ten experiments employing different triplets of mental tasks are studied for each subject. The findings show that the average BCI-FA outputs for four subjects gave less than 6% of error using the best triplets of mental tasks identified from the classification performances of FA. This implies that the BCI-FA can be successfully used with a tri-state switching device. As an application, a proposed tri-state Morse code scheme could be utilized to translate the outputs of this BCI-FA design into English letters. In this scheme, the three BCI-FA outputs correspond to a dot and a dash, which are the two basic Morse code alphabets and a space to denote the end (or beginning) of a dot or a dash. The construction of English letters using this tri-state Morse code scheme is determined only by the sequence of mental tasks and is independent of the time duration of each mental task. This is especially useful for constructing letters that are represented as multiple dots or dashes. This combination of BCI-FA design and the tri-state Morse code scheme could be developed as a communication system for paralyzed patients.

Matched MeSH terms: Electroencephalography/methods
Fulltext Multicenter prospective study on predictors of short-term outcome in disorders of consciousness

Estraneo A, Fiorenza S, Magliacano A, Formisano R, Mattia D, Grippo A, et al.

Neurology, 2020 09 15;95(11):e1488-e1499.
PMID: 32661102 DOI: 10.1212/WNL.0000000000010254

OBJECTIVE: This international multicenter, prospective, observational study aimed at identifying predictors of short-term clinical outcome in patients with prolonged disorders of consciousness (DoC) due to acquired severe brain injury.
METHODS: Patients in vegetative state/unresponsive wakefulness syndrome (VS/UWS) or in minimally conscious state (MCS) were enrolled within 3 months from their brain injury in 12 specialized medical institutions. Demographic, anamnestic, clinical, and neurophysiologic data were collected at study entry. Patients were then followed up for assessing the primary outcome, that is, clinical diagnosis according to standardized criteria at 6 months postinjury.
RESULTS: We enrolled 147 patients (44 women; mean age 49.4 [95% confidence interval 46.1-52.6] years; VS/UWS 71, MCS 76; traumatic 55, vascular 56, anoxic 36; mean time postinjury 59.6 [55.4-63.6] days). The 6-month follow-up was complete for 143 patients (VS/UWS 70; MCS 73). With respect to study entry, the clinical diagnosis improved in 72 patients (VS/UWS 27; MCS 45). Younger age, shorter time postinjury, higher Coma Recovery Scale-Revised total score, and presence of EEG reactivity to eye opening at study entry predicted better outcome, whereas etiology, clinical diagnosis, Disability Rating Scale score, EEG background activity, acoustic reactivity, and P300 on event-related potentials were not associated with outcome.
CONCLUSIONS: Multimodal assessment could identify patients with higher likelihood of clinical improvement in order to help clinicians, families, and funding sources with various aspects of decision-making. This multicenter, international study aims to stimulate further research that drives international consensus regarding standardization of prognostic procedures for patients with DoC.

Matched MeSH terms: Electroencephalography/trends
Removal of BCG artefact from concurrent fMRI-EEG recordings based on EMD and PCA

Javed E, Faye I, Malik AS, Abdullah JM

J Neurosci Methods, 2017 11 01;291:150-165.
PMID: 28842191 DOI: 10.1016/j.jneumeth.2017.08.020

BACKGROUND: Simultaneous electroencephalography (EEG) and functional magnetic resonance image (fMRI) acquisitions provide better insight into brain dynamics. Some artefacts due to simultaneous acquisition pose a threat to the quality of the data. One such problematic artefact is the ballistocardiogram (BCG) artefact.
METHODS: We developed a hybrid algorithm that combines features of empirical mode decomposition (EMD) with principal component analysis (PCA) to reduce the BCG artefact. The algorithm does not require extra electrocardiogram (ECG) or electrooculogram (EOG) recordings to extract the BCG artefact.
RESULTS: The method was tested with both simulated and real EEG data of 11 participants. From the simulated data, the similarity index between the extracted BCG and the simulated BCG showed the effectiveness of the proposed method in BCG removal. On the other hand, real data were recorded with two conditions, i.e. resting state (eyes closed dataset) and task influenced (event-related potentials (ERPs) dataset). Using qualitative (visual inspection) and quantitative (similarity index, improved normalized power spectrum (INPS) ratio, power spectrum, sample entropy (SE)) evaluation parameters, the assessment results showed that the proposed method can efficiently reduce the BCG artefact while preserving the neuronal signals.
COMPARISON WITH EXISTING METHODS: Compared with conventional methods, namely, average artefact subtraction (AAS), optimal basis set (OBS) and combined independent component analysis and principal component analysis (ICA-PCA), the statistical analyses of the results showed that the proposed method has better performance, and the differences were significant for all quantitative parameters except for the power and sample entropy.
CONCLUSIONS: The proposed method does not require any reference signal, prior information or assumption to extract the BCG artefact. It will be very useful in circumstances where the reference signal is not available.

Matched MeSH terms: Electroencephalography/methods*

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