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Fulltext Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion

Zafar R, Dass SC, Malik AS

PLoS One, 2017;12(5):e0178410.
PMID: 28558002 DOI: 10.1371/journal.pone.0178410

Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an important technique, especially for brain-computer interface applications. In this study, a novel algorithm is proposed to decode brain activity associated with different types of images. In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity. The proposed algorithm takes input data from multichannel EEG time-series, which is also known as multivariate pattern analysis. Comprehensive analysis was conducted using data from 30 participants. The results from the proposed method are compared with current recognized feature extraction and classification/prediction techniques. The wavelet transform-support vector machine method is the most popular currently used feature extraction and prediction method. This method showed an accuracy of 65.7%. However, the proposed method predicts the novel data with improved accuracy of 79.9%. In conclusion, the proposed algorithm outperformed the current feature extraction and prediction method.
Fulltext Acute salicylism due to accidental ingestion of a traditional medicine

Malik AS, Zabidi MH, Noor AR

Singapore Med J, 1994 Apr;35(2):215-6.
PMID: 7939826

Traditional medicine is practised to some degree in all cultures. Many different types of herbal preparations and "oils" are widely used in Malaysia, too. We report a case of acute salicylism due to accidental ingestion of one brand of such oils. Compulsory labelling of traditional drugs with their chemical ingredients is suggested for proper and timely management of such cases.
Misidentification of toxigenic Corynebacterium diphtheriae as a Corynebacterium species with low virulence in a child with endocarditis

Pennie RA, Malik AS, Wilcox L

J Clin Microbiol, 1996 May;34(5):1275-6.
PMID: 8727917

A 6-year-old boy presented to a university hospital in Malaysia with infective endocarditis complicating cyanotic congenital heart disease. Blood cultures showed a gram-positive, aerobic, coryneform-like bacillus identified by the hospital laboratory as Corynebacterium xerosis, but a reference laboratory identified the organism as a toxigenic strain of Corynebacterium diphtheriae. The two laboratories concurred on all biochemical test results except for sucrose fermentation.
Fulltext Clinical experience of medical students at University Sains Malaysia

Quah BS, Malik AS, Simpson H

Malays J Med Sci, 2000 Jan;7(1):27-32.
PMID: 22844212 MyJurnal

Experience of acute medical, surgical conditions, and clinical procedures of undergraduate students were assessed via a questionnaire survey during the final week of the 1993/1998 programme at the School of Medical Sciences, Univestiti Sains Malaysia. Individual performances were assessed by a scoring system. One hundred and twenty four students responded, (response rate 97%). More than 90% had seen myocardial infarction, cerebrovascular accident, pneumonia, respiratory distress, gastroenteritis, coma, and snake bite. Less than 33% had witnessed acute psychosis, diabetic ketoacidosis, acute hepatic failure, status epilepticus, near drowning, hypertensive encephalopathy, acute haemolysis or child abuse.Acute surgical/obstetrics cases, seen by >90% students, included fracture of long bones, head injury, acute abdominal pain, malpresentation and foetal distress. Less than 33% had observed epistaxis, sudden loss of vision, peritonitis or burns. Among operations only herniorrhaphy, Caesarian section, internal fixation of fracture and cataract extraction were seen by >80% students. The main deficits in clinical procedures are in rectal and vaginal examinations, urine collection and microscopic examinations. The performance of individual students, assessed by a scoring system, showed 15 students had unacceptably low scores (<149/230, 50%), 37 had good scores (>181.4/230, 70%) and 5 had superior scores (197.6/230, 80%).
A Novel Approach Based on Data Redundancy for Feature Extraction of EEG Signals

Amin HU, Malik AS, Kamel N, Hussain M

Brain Topogr, 2016 Mar;29(2):207-17.
PMID: 26613724 DOI: 10.1007/s10548-015-0462-2

Feature extraction and classification for electroencephalogram (EEG) in medical applications is a challenging task. The EEG signals produce a huge amount of redundant data or repeating information. This redundancy causes potential hurdles in EEG analysis. Hence, we propose to use this redundant information of EEG as a feature to discriminate and classify different EEG datasets. In this study, we have proposed a JPEG2000 based approach for computing data redundancy from multi-channels EEG signals and have used the redundancy as a feature for classification of EEG signals by applying support vector machine, multi-layer perceptron and k-nearest neighbors classifiers. The approach is validated on three EEG datasets and achieved high accuracy rate (95-99 %) in the classification. Dataset-1 includes the EEG signals recorded during fluid intelligence test, dataset-2 consists of EEG signals recorded during memory recall test, and dataset-3 has epileptic seizure and non-seizure EEG. The findings demonstrate that the approach has the ability to extract robust feature and classify the EEG signals in various applications including clinical as well as normal EEG patterns.
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.
Fulltext Hyperspectral venous image quality assessment for optimum illumination range selection based on skin tone characteristics

Shahzad A, Saad MN, Walter N, Malik AS, Meriaudeau F

Biomed Eng Online, 2014;13:109.
PMID: 25087016 DOI: 10.1186/1475-925X-13-109

Subcutaneous veins localization is usually performed manually by medical staff to find suitable vein to insert catheter for medication delivery or blood sample function. The rule of thumb is to find large and straight enough vein for the medication to flow inside of the selected blood vessel without any obstruction. The problem of peripheral difficult venous access arises when patient's veins are not visible due to any reason like dark skin tone, presence of hair, high body fat or dehydrated condition, etc.
Physiological assessment of in vivo human knee articular cartilage using sodium MR imaging at 1.5 T

Hani AF, Kumar D, Malik AS, Razak R

Magn Reson Imaging, 2013 Sep;31(7):1059-67.
PMID: 23731535 DOI: 10.1016/j.mri.2013.01.007

Osteoarthritis is a common joint disorder that is most prevalent in the knee joint. Knee osteoarthritis (OA) can be characterized by the gradual loss of articular cartilage (AC). Formation of lesion, fissures and cracks on the cartilage surface has been associated with degenerative AC and can be measured by morphological assessment. In addition, loss of proteoglycan from extracellular matrix of the AC can be measured at early stage of cartilage degradation by physiological assessment. In this case, a biochemical phenomenon of cartilage is used to assess the changes at early degeneration of AC. In this paper, a method to measure local sodium concentration in AC due to proteoglycan has been investigated. A clinical 1.5-T magnetic resonance imaging (MRI) with multinuclear spectroscopic facility is used to acquire sodium images and quantify local sodium content of AC. An optimised 3D gradient-echo sequence with low echo time has been used for MR scan. The estimated sodium concentration in AC region from four different data sets is found to be ~225±19mmol/l, which matches the values that has been reported for the normal AC. This study shows that sodium images acquired at clinical 1.5-T MRI system can generate an adequate quantitative data that enable the estimation of sodium concentration in AC. We conclude that this method is potentially suitable for non-invasive physiological (sodium content) measurement of articular cartilage.
Fulltext Acne analysis, grading and computational assessment methods: an overview

Ramli R, Malik AS, Hani AF, Jamil A

Skin Res Technol, 2012 Feb;18(1):1-14.
PMID: 21605170 DOI: 10.1111/j.1600-0846.2011.00542.x

INTRODUCTION: This paper presents a comprehensive review of acne grading and measurement. Acne is a chronic disorder of the pilosebaceous units, with excess sebum production, follicular epidermal hyperproliferation, inflammation and Propionibacterium acnes activity. Most patients are affected with acne vulgaris, which is the prevalent type of acne. Acne vulgaris consists of comedones (whitehead and blackhead), papules, pustules, nodules and cysts.
OBJECTIVES: To review and identify the issues for acne vulgaris grading and computational assessment methods. To determine the future direction for addressing the identified issues.
METHODS: There are two main methods of assessment for acne severity grading, namely, lesion counting and comparison of patient with a photographic standard. For the computational assessment method, the emphasis is on computational imaging techniques.
RESULTS: Current acne grading methods are very time consuming and tedious. Generally, they rely on approximation for counting lesions and hence the assessment is quite subjective, with both inter and intra-observer variability. It is important to accurately assess acne grade to evaluate its severity as this influences treatment selection and assessment of response to therapy. This will further help in better disease management and more efficacious treatment.
CONCLUSION: Semi-automated or automated methods based on computational imaging techniques should be devised for acne grade assessment.
Velocity field measurement of a round jet using quantitative schlieren

Iffa ED, Aziz AR, Malik AS

Appl Opt, 2011 Feb 10;50(5):618-25.
PMID: 21343981 DOI: 10.1364/AO.50.000618

This paper utilizes the background oriented schlieren (BOS) technique to measure the velocity field of a variable density round jet. The density field of the jet is computed based on the light deflection created during the passage of light through the understudy jet. The deflection vector estimation was carried out using phase-based optical flow algorithms. The density field is further exploited to extract the axial and radial velocity vectors with the aid of continuity and energy equations. The experiment is conducted at six different jet-exit temperature values. Additional turbulence parameters, such as velocity variance and power spectral density of the vector field, are also computed. Finally, the measured velocity parameters are compared with the hot wire anemometer measurements and their correlation is displayed.
Susceptibility pattern of Streptococcus pneumoniae among pre-school children in Kota Bharu, Malaysia

Malik AS, Ismail A, Pennie RA, Naidu JV

J Trop Pediatr, 1998 02;44(1):10-4.
PMID: 9538599 DOI: 10.1093/tropej/44.1.10

Streptococcus pneumoniae (S. pneumoniae) is the most common bacterial cause of pneumonia, meningitis, and otitis media, with the highest incidence among young children and the elderly. S. pneumoniae was once routinely susceptible to penicillin, but since the mid-1980s the incidence of resistance to penicillin and other antimicrobial agents has been increasing all over the world. To optimize empirical regimens and initial therapy for S. pneumoniae infections, clinical healthcare providers must be informed about the prevalence and pattern of drug resistance among the isolates in their communities. No such data are available for the Malaysian population. Therefore, this study was designed to determine the antibiotic susceptibility pattern of S. pneumoniae among colonized pre-school children in Kota Bharu, Malaysia. Pharyngeal swabs were collected from children 1 month to 6 years of age. S. pneumoniae isolates were identified according to the standard and tested for penicillin resistance with a 1-microgram oxacillin disk by the Kirby-Bauer disk diffusion methods. Of 355 nasopharyngeal specimens obtained from kindergarten students, in-patients and pediatric clinics over a period of 1 year, S. pneumoniae was isolated from 36 (10 per cent). All isolates, except one, were susceptible to penicillin. The resistant isolates was susceptible to erythromycin, chloramphenicol and cephalosporins.
Study site: kindergarten, schools, pediatric outpatients clinics, and in-patient wards of Hospital Universiti Sains Malaysia (HUSM), Kelantan, Malaysia.
Fulltext Tuberous sclerosis complex: a review with a study of eight cases

Malik AS, Hussin ZA, Shriwas SR, Kasim ZM

Med J Malaysia, 1994 Dec;49(4):375-84.
PMID: 7674974

This paper reviews the subject of tuberous sclerosis complex and presents data in eight cases of this condition, admitted to Hospital Universiti Sains Malaysia over a period of 8.5 years. The average age at presentation was 53 months. Seizures were the most common presenting feature. Male to female ratio was 3:1. Family history was present in four patients. All of the patients had one or more skin lesions. Six of them had retinal in six patients. Two patients had renal angiomyolipomas. A high index of hamartomas. Cardiac tumours were found in two patients. Multiple subependymal hamartomas were detected in six patients. Two patients had renal angiomyolipomas. A high index of suspicion and a detailed physical examination is required to diagnose this rare disorder.
A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD)

Mumtaz W, Ali SSA, Yasin MAM, Malik AS

Med Biol Eng Comput, 2018 Feb;56(2):233-246.
PMID: 28702811 DOI: 10.1007/s11517-017-1685-z

Major depressive disorder (MDD), a debilitating mental illness, could cause functional disabilities and could become a social problem. An accurate and early diagnosis for depression could become challenging. This paper proposed a machine learning framework involving EEG-derived synchronization likelihood (SL) features as input data for automatic diagnosis of MDD. It was hypothesized that EEG-based SL features could discriminate MDD patients and healthy controls with an acceptable accuracy better than measures such as interhemispheric coherence and mutual information. In this work, classification models such as support vector machine (SVM), logistic regression (LR) and Naïve Bayesian (NB) were employed to model relationship between the EEG features and the study groups (MDD patient and healthy controls) and ultimately achieved discrimination of study participants. The results indicated that the classification rates were better than chance. More specifically, the study resulted into SVM classification accuracy = 98%, sensitivity = 99.9%, specificity = 95% and f-measure = 0.97; LR classification accuracy = 91.7%, sensitivity = 86.66%, specificity = 96.6% and f-measure = 0.90; NB classification accuracy = 93.6%, sensitivity = 100%, specificity = 87.9% and f-measure = 0.95. In conclusion, SL could be a promising method for diagnosing depression. The findings could be generalized to develop a robust CAD-based tool that may help for clinical purposes.
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.
Fulltext Towards health monitoring using remote heart rate measurement using digital camera: A feasibility study

Hassan MA, Malik AS, Fofi D, Karasfi B, Meriaudeau F

Measurement : journal of the International Measurement Confederation, 2020 Jan;149:106804.
PMID: 32287815 DOI: 10.1016/j.measurement.2019.07.032

The paper presents a feasibility study for heart rate measurement using a digital camera to perform health monitoring. The feasibility study investigates the reliability of the state of the art heart rate measuring methods in realistic situations. Therefore, an experiment was designed and carried out on 45 subjects to investigate the effects caused by illumination, motion, skin tone, and distance variance. The experiment was conducted for two main scenarios; human-computer interaction scenario and health monitoring scenario. The human-computer scenario investigated the effects caused by illumination variance, motion variance, and skin tone variance. The health monitoring scenario investigates the feasibility of health monitoring at public spaces (i.e. airports, subways, malls). Five state of the art heart rate measuring methods were re-implemented and tested with the feasibility study database. The results were compared with ground truth to estimate the heart rate measurement error. The heart rate measurement error was analyzed using mean error, standard deviation; root means square error and Pearson correlation coefficient. The findings of this experiment inferred promising results for health monitoring of subjects standing at a distance of 500 cm.
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.
Novel health monitoring method using an RGB camera

Hassan MA, Malik AS, Fofi D, Saad N, Meriaudeau F

Biomed Opt Express, 2017 Nov 01;8(11):4838-4854.
PMID: 29188085 DOI: 10.1364/BOE.8.004838

In this paper we present a novel health monitoring method by estimating the heart rate and respiratory rate using an RGB camera. The heart rate and the respiratory rate are estimated from the photoplethysmography (PPG) and the respiratory motion. The method mainly operates by using the green spectrum of the RGB camera to generate a multivariate PPG signal to perform multivariate de-noising on the video signal to extract the resultant PPG signal. A periodicity based voting scheme (PVS) was used to measure the heart rate and respiratory rate from the estimated PPG signal. We evaluated our proposed method with a state of the art heart rate measuring method for two scenarios using the MAHNOB-HCI database and a self collected naturalistic environment database. The methods were furthermore evaluated for various scenarios at naturalistic environments such as a motion variance session and a skin tone variance session. Our proposed method operated robustly during the experiments and outperformed the state of the art heart rate measuring methods by compensating the effects of the naturalistic environment.
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.
Fulltext The Influences of Emotion on Learning and Memory

Tyng CM, Amin HU, Saad MNM, Malik AS

Front Psychol, 2017;8:1454.
PMID: 28883804 DOI: 10.3389/fpsyg.2017.01454

Emotion has a substantial influence on the cognitive processes in humans, including perception, attention, learning, memory, reasoning, and problem solving. Emotion has a particularly strong influence on attention, especially modulating the selectivity of attention as well as motivating action and behavior. This attentional and executive control is intimately linked to learning processes, as intrinsically limited attentional capacities are better focused on relevant information. Emotion also facilitates encoding and helps retrieval of information efficiently. However, the effects of emotion on learning and memory are not always univalent, as studies have reported that emotion either enhances or impairs learning and long-term memory (LTM) retention, depending on a range of factors. Recent neuroimaging findings have indicated that the amygdala and prefrontal cortex cooperate with the medial temporal lobe in an integrated manner that affords (i) the amygdala modulating memory consolidation; (ii) the prefrontal cortex mediating memory encoding and formation; and (iii) the hippocampus for successful learning and LTM retention. We also review the nested hierarchies of circular emotional control and cognitive regulation (bottom-up and top-down influences) within the brain to achieve optimal integration of emotional and cognitive processing. This review highlights a basic evolutionary approach to emotion to understand the effects of emotion on learning and memory and the functional roles played by various brain regions and their mutual interactions in relation to emotional processing. We also summarize the current state of knowledge on the impact of emotion on memory and map implications for educational settings. In addition to elucidating the memory-enhancing effects of emotion, neuroimaging findings extend our understanding of emotional influences on learning and memory processes; this knowledge may be useful for the design of effective educational curricula to provide a conducive learning environment for both traditional "live" learning in classrooms and "virtual" learning through online-based educational technologies.
Single-trial extraction of event-related potentials (ERPs) and classification of visual stimuli by ensemble use of discrete wavelet transform with Huffman coding and machine learning techniques

Amin HU, Ullah R, Reza MF, Malik AS

J Neuroeng Rehabil, 2023 Jun 02;20(1):70.
PMID: 37269019 DOI: 10.1186/s12984-023-01179-8

BACKGROUND: Presentation of visual stimuli can induce changes in EEG signals that are typically detectable by averaging together data from multiple trials for individual participant analysis as well as for groups or conditions analysis of multiple participants. This study proposes a new method based on the discrete wavelet transform with Huffman coding and machine learning for single-trial analysis of evenal (ERPs) and classification of different visual events in the visual object detection task.
METHODS: EEG single trials are decomposed with discrete wavelet transform (DWT) up to the [Formula: see text] level of decomposition using a biorthogonal B-spline wavelet. The coefficients of DWT in each trial are thresholded to discard sparse wavelet coefficients, while the quality of the signal is well maintained. The remaining optimum coefficients in each trial are encoded into bitstreams using Huffman coding, and the codewords are represented as a feature of the ERP signal. The performance of this method is tested with real visual ERPs of sixty-eight subjects.
RESULTS: The proposed method significantly discards the spontaneous EEG activity, extracts the single-trial visual ERPs, represents the ERP waveform into a compact bitstream as a feature, and achieves promising results in classifying the visual objects with classification performance metrics: accuracies 93.60[Formula: see text], sensitivities 93.55[Formula: see text], specificities 94.85[Formula: see text], precisions 92.50[Formula: see text], and area under the curve (AUC) 0.93[Formula: see text] using SVM and k-NN machine learning classifiers.
CONCLUSION: The proposed method suggests that the joint use of discrete wavelet transform (DWT) with Huffman coding has the potential to efficiently extract ERPs from background EEG for studying evoked responses in single-trial ERPs and classifying visual stimuli. The proposed approach has O(N) time complexity and could be implemented in real-time systems, such as the brain-computer interface (BCI), where fast detection of mental events is desired to smoothly operate a machine with minds.

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