Proposed Feature Extraction Research Articles

An automatic feature extraction technique from the images of granular parakeratosis disease

The largest and most vital part of the human body is skin and any change in the features of skin is termed as a skin lesion. The paper considers granular parakeratosis lesion that is an epidermal reaction occurring due to the disorder of keratinization, and mainly seen in intertriginous areas. The manual inspection of the lesion features is a bit cumbersome due to which an automated system is proposed in this paper. The main goal is to determine the size and depth of granular parakeratosis lesions using the proposed ensemble algorithm, partition clustering and region properties method. As a flow of the proposed model, segmentation is done using U-net with binary cross entropy, features are extracted using partition clustering and region properties method, and classification is done using SVM 10-fold model. The proposed feature extraction method estimates the depth and absolute size of K lesions in each image by predicting the absolute height and width of the lesion in terms of pixel square. After extracting the features, classification is done, thereby obtaining an accuracy of 95%, sensitivity and specificity of 100%. The proposed model provides better performance compared to state-of-the-art models. The main application of this automated system is in dermatology field where some skin lesions have same features which makes the experts to diagnose the disease incorrectly. If the proposed system is incorporated, diagnosis can be done in an effective manner considering all the relevant features.

COVID-19 respiratory sound analysis and classification using audio textures

Since the COVID-19 outbreak, a major scientific effort has been made by researchers and companies worldwide to develop a digital diagnostic tool to screen this disease through some biomedical signals, such as cough, and speech. Joint time–frequency feature extraction techniques and machine learning (ML)-based models have been widely explored in respiratory diseases such as influenza, pertussis, and COVID-19 to find biomarkers from human respiratory system-generated acoustic sounds. In recent years, a variety of techniques for discriminating textures and computationally efficient local texture descriptors have been introduced, such as local binary patterns and local ternary patterns, among others. In this work, we propose an audio texture analysis of sounds emitted by subjects in suspicion of COVID-19 infection using time–frequency spectrograms. This approach of the feature extraction method has not been widely used for biomedical sounds, particularly for COVID-19 or respiratory diseases. We hypothesize that this textural sound analysis based on local binary patterns and local ternary patterns enables us to obtain a better classification model by discriminating both people with COVID-19 and healthy subjects. Cough, speech, and breath sounds from the INTERSPEECH 2021 ComParE and Cambridge KDD databases have been processed and analyzed to evaluate our proposed feature extraction method with ML techniques in order to distinguish between positive or negative for COVID-19 sounds. The results have been evaluated in terms of an unweighted average recall (UAR). The results show that the proposed method has performed well for cough, speech, and breath sound classification, with a UAR up to 100.00%, 60.67%, and 95.00%, respectively, to infer COVID-19 infection, which serves as an effective tool to perform a preliminary screening of COVID-19.

Vision-based gait analysis for real-time Parkinson disease identification and diagnosis system

ABSTRACT Computer-assisted Parkinson’s disease-specific gait pattern recognition has gained more attention in the past decade due to its extensive application. In this research study, vision-based gait feature extraction is obtained from the observed skeleton points to support the real-time Parkinson disease prediction and diagnosis in the smart healthcare environment. So, a novel kernel-based principal component analysis (KPCA) is introduced for establishing respective feature extraction and dimensionality reduction on the patient’s video data. In this research study, a vision-based Parkinson disease identification system (VPDIS) is developed with a feature-weighted minimum distance classifier model to support the clinical assessment of Parkinson’s disease. At the time of experimentation, a steady-state walking style of the patient was captured using the cameras fixed in the smart healthcare environment. Then, the accumulated walking frames from the remote patients were transformed into the required binary silhouettes for the sake of noise minimisation and compression purpose. The resulting experimentation shows that the proposed feature extraction approach has significant improvements on the recognition of target gait patterns from the video-based gait analysis of Parkinson’s and normal patients. Accordingly, the proposed VPDIS using feature-weighted minimum distance classifier model provides better prediction time and classification accuracy against the existing healthcare systems that is developed using support vector machine and ensemble learning classifier models.

Multimodal Emotional Classification Based on Meaningful Learning

Emotion recognition has become one of the most researched subjects in the scientific community, especially in the human–computer interface field. Decades of scientific research have been conducted on unimodal emotion analysis, whereas recent contributions concentrate on multimodal emotion recognition. These efforts have achieved great success in terms of accuracy in diverse areas of Deep Learning applications. To achieve better performance for multimodal emotion recognition systems, we exploit Meaningful Neural Network Effectiveness to enable emotion prediction during a conversation. Using the text and the audio modalities, we proposed feature extraction methods based on Deep Learning. Then, the bimodal modality that is created following the fusion of the text and audio features is used. The feature vectors from these three modalities are assigned to feed a Meaningful Neural Network to separately learn each characteristic. Its architecture consists of a set of neurons for each component of the input vector before combining them all together in the last layer. Our model was evaluated on a multimodal and multiparty dataset for emotion recognition in conversation MELD. The proposed approach reached an accuracy of 86.69%, which significantly outperforms all current multimodal systems. To sum up, several evaluation techniques applied to our work demonstrate the robustness and superiority of our model over other state-of-the-art MELD models.

Diagnosis of cardiac abnormalities based on phonocardiogram using a novel fuzzy matching feature extraction method

BackgroundThe diagnosis of cardiac abnormalities based on heart sound signal is a research hotspot in recent years. The early diagnosis of cardiac abnormalities has a crucial significance for the treatment of heart diseases.MethodsFor the sake of achieving more practical clinical applications of automatic recognition of cardiac abnormalities, here we proposed a novel fuzzy matching feature extraction method. First of all, a group of Gaussian wavelets are selected and then optimized based on a template signal. Convolutional features of test signal and the template signal are then computed. Matching degree and matching energy features between template signal and test signal in time domain and frequency domain are then extracted. To test performance of proposed feature extraction method, machine learning algorithms such as K-nearest neighbor, support vector machine, random forest and multilayer perceptron with grid search parameter optimization are constructed to recognize heart disease using the extracted features based on phonocardiogram signals.ResultsAs a result, we found that the best classification accuracy of random forest reaches 96.5% under tenfold cross validation using the features extracted by the proposed method. Further, Mel-Frequency Cepstral Coefficients of phonocardiogram signals combing with features extracted by our algorithm are evaluated. Accuracy, sensitivity and specificity of integrated features reaches 99.0%, 99.4% and 99.7% respectively when using support vector machine, which achieves the best performance among all reported algorithms based on the same dataset. On several common features, we used independent sample t-tests. The results revealed that there are significant differences (p < 0.05) between 5 categories.ConclusionIt can be concluded that our proposed fuzzy matching feature extraction method is a practical approach to extract powerful and interpretable features from one-dimensional signals for heart sound diagnostics and other pattern recognition task.

Automated apoptosis identification in fluorescence imaging of nucleus based on histogram of oriented gradients of high-frequency wavelet coefficients

The automatic and accurate identification of apoptosis facilitates large-scale cell analysis. Most identification approaches using nucleus fluorescence imaging are based on specific morphological parameters. However, these parameters cannot completely describe nuclear morphology, thus limiting the identification accuracy of models. This paper proposes a new feature extraction method to improve the performance of the model for apoptosis identification. The proposed method uses a histogram of oriented gradient (HOG) of high-frequency wavelet coefficients to extract internal and edge texture information. The HOG vectors are classified using support vector machine. The experimental results demonstrate that the proposed feature extraction method well performs apoptosis identification, attaining [Formula: see text] accuracy with low cost in terms of time. We confirmed that our method has potential applications to cell biology research.

Detecting Coronary Artery Disease from Computed Tomography Images Using a Deep Learning Technique

In recent times, coronary artery disease (CAD) has become one of the leading causes of morbidity and mortality across the globe. Diagnosing the presence and severity of CAD in individuals is essential for choosing the best course of treatment. Presently, computed tomography (CT) provides high spatial resolution images of the heart and coronary arteries in a short period. On the other hand, there are many challenges in analyzing cardiac CT scans for signs of CAD. Research studies apply machine learning (ML) for high accuracy and consistent performance to overcome the limitations. It allows excellent visualization of the coronary arteries with high spatial resolution. Convolutional neural networks (CNN) are widely applied in medical image processing to identify diseases. However, there is a demand for efficient feature extraction to enhance the performance of ML techniques. The feature extraction process is one of the factors in improving ML techniques’ efficiency. Thus, the study intends to develop a method to detect CAD from CT angiography images. It proposes a feature extraction method and a CNN model for detecting the CAD in minimum time with optimal accuracy. Two datasets are utilized to evaluate the performance of the proposed model. The present work is unique in applying a feature extraction model with CNN for CAD detection. The experimental analysis shows that the proposed method achieves 99.2% and 98.73% prediction accuracy, with F1 scores of 98.95 and 98.82 for benchmark datasets. In addition, the outcome suggests that the proposed CNN model achieves the area under the receiver operating characteristic and precision-recall curve of 0.92 and 0.96, 0.91 and 0.90 for datasets 1 and 2, respectively. The findings highlight that the performance of the proposed feature extraction and CNN model is superior to the existing models.

Real-time classification of aluminum metal scrap with laser-induced breakdown spectroscopy using deep and other machine learning approaches

In the recycling industry, the use of deep spectral convolutional networks for the purpose of material classification and composition estimation is still limited, despite the great opportunities of these techniques. In this study, the use of Laser-Induced Breakdown Spectroscopy (LIBS), Machine Learning (ML), and Deep Learning (DL) for the three-way sorting of Aluminum (Al) is proposed. Two sample sets of Al scrap are used: one containing 733 pieces for pre-training and validation with a ground truth of X-Ray Fluorescence (XRF), and the second containing 210 pieces for testing for unknown compositions. The proposed method comprises a denoising system combined with a method that extracts 145 features from the raw LIBS spectra. Further, three ML algorithms are assessed to identify the best-performing one to classify unknown pieces of aluminum post-consumer scrap into three commercially interesting output classes. The classified pieces are weighed, melted, and analyzed using spark analysis. Finally, to optimize the best-performing ML system, three state-of-the-art denoising and three feature extraction networks are pre-trained for learning the baseline correction and the proposed feature extraction. Transfer Learning from the six pre-trained networks is applied to create and evaluate 24 end-to-end DL models to classify Al in real-time from >200 spectra simultaneously. The end-to-end DL scheme shows the advantages of learning and denoising the spectra, allowing the transfer of traditional spectral analysis knowledge and the proposed feature extraction into DL, where the network learns from the entire spectrum. The best results for ML and DL were obtained with Random Forest processing one spectrum in 150 ms and BPNN+GHOSTNET(Fine-tuning) processing 200 spectra in 9 ms, which achieved 0.80 Precision, 0.81 Recall, 0.80 F1-score, and 0.80 Precision, 0.79 Recall, 0.79 F1-score, respectively.

Feature Extraction using Lexicon on the Emotion Recognition Dataset of Indonesian Text

Text Mining is a part of Neural Language Processing (NLP), also known as text analytics. Text mining includes sentiment analysis and emotion analysis which are often used in analysis on social media, news, or other media in written form. The emotional breakdown is a level of sentiment analysis that categorises text into negative, neutral, and positive sentiments. Emotion is categorized into several classes, In this study, emotion is categorized into 5 classes namely anger, fear, happiness, love, and sadness. This study proposed feature extraction using Lexicon and TF-IDF on the emotion recognition dataset of Indonesian texts. InSet Lexicon Dictionary is used as the corpus in performing the feature extraction. Therefore, InSet Lexicon was chosen as the dictionary to perform feature extraction in this study. The results show that InSet Lexicon has poor performance in feature extraction by showing an accuracy of 30%, while TF-IDF is 62%.

Stockwell transform and semi-supervised feature selection from deep features for classification of BCI signals

Over the past few years, the processing of motor imagery (MI) electroencephalography (EEG) signals has been attracted for developing brain-computer interface (BCI) applications, since feature extraction and classification of these signals are extremely difficult due to the inherent complexity and tendency to artifact properties of them. The BCI systems can provide a direct interaction pathway/channel between the brain and a peripheral device, hence the MI EEG-based BCI systems seem crucial to control external devices for patients suffering from motor disabilities. The current study presents a semi-supervised model based on three-stage feature extraction and machine learning algorithms for MI EEG signal classification in order to improve the classification accuracy with smaller number of deep features for distinguishing right- and left-hand MI tasks. Stockwell transform is employed at the first phase of the proposed feature extraction method to generate two-dimensional time–frequency maps (TFMs) from one-dimensional EEG signals. Next, the convolutional neural network (CNN) is applied to find deep feature sets from TFMs. Then, the semi-supervised discriminant analysis (SDA) is utilized to minimize the number of descriptors. Finally, the performance of five classifiers, including support vector machine, discriminant analysis, k-nearest neighbor, decision tree, random forest, and the fusion of them are compared. The hyperparameters of SDA and mentioned classifiers are optimized by Bayesian optimization to maximize the accuracy. The presented model is validated using BCI competition II dataset III and BCI competition IV dataset 2b. The performance metrics of the proposed method indicate its efficiency for classifying MI EEG signals.

Mental State Classification Using Multi-Graph Features.

We consider the problem of extracting features from passive, multi-channel electroencephalogram (EEG) devices for downstream inference tasks related to high-level mental states such as stress and cognitive load. Our proposed feature extraction method uses recently developed spectral-based multi-graph tools and applies them to the time series of graphs implied by the statistical dependence structure (e.g., correlation) amongst the multiple sensors. We study the features in the context of two datasets each consisting of at least 30 participants and recorded using multi-channel EEG systems. We compare the classification performance of a classifier trained on the proposed features to a classifier trained on the traditional band power-based features in three settings and find that the two feature sets offer complementary predictive information. We conclude by showing that the importance of particular channels and pairs of channels for classification when using the proposed features is neuroscientifically valid.

Short-Term Electrical Load Forecasting With Multidimensional Feature Extraction

Accurate short-term load forecasting (STLF) is required for reliable power system operations. Nevertheless, load forecasting remains a challenge owing to the high dimensionality and volatility of electrical load data as time series. In this study, a feature extraction framework for electrical load and other complementary data as a multivariate time series is proposed. The proposed framework consists of tagging and embedding processes that extract patterns from the multivariate time series as tags and capture their temporal and dimensional relations. In the embedding process, a network model that embeds the tags is deliberately designed with a convolutional layer in a multi-output structure based on mathematical analysis. Furthermore, a deep learning-based STLF model is constructed with the proposed feature extraction framework. The performance of the proposed STLF model for day-ahead load forecasting is evaluated on a publicly available set of real electricity demand data. The experimental results verify that the proposed approach reduces the root mean squared error by 5% to 12%. This improvement in load forecasts can benefit power grid operations as it provides more accurate expectations on the behaviors of the power grid in short term, which can be utilized in power grid applications, such as power dispatch and scheduling.

Network Theory Based EHG Signal Analysis and its Application in Preterm Prediction.

Preterm birth is the leading cause of neonatal morbidity and mortality. Early identification of high-risk patients followed by medical interventions is essential to the prevention of preterm birth. Based on the relationship between uterine contraction and the fundamental electrical activities of muscles, we extracted effective features from EHG signals recorded from pregnant women, and use them to train classifiers with the purpose of providing high precision in classifying term and preterm pregnancies. To characterize changes from irregularity to coherence of the uterine activity during the whole pregnancy, network representations of the original electrohysterogram (EHG) signals are established by applying the Horizontal Visibility Graph (HVG) algorithm, from which we extract network degree density and distribution, clustering coefficient and assortativity coefficient. Concerns on the interferences of different noise sources embedded in the EHG signal, we apply Short-Time Fourier Transform (STFT) to expand the original signal in the time-frequency domain. This allows a network representation and the extraction of related features on each frequency component. Feature selection algorithms are then used to filter out unrelated frequency components. We further apply the proposed feature extraction method to EHG signals available in the Term-Preterm EHG database (TPEHG), and use them to train classifiers. We adopt the Partition-Synthesis scheme which splits the original imbalanced dataset into two sets, and synthesizes artificial samples separately within each subset to solve the problem of dataset imbalance. The optimally selected network-based features, not only contribute to the identification of the essential frequency components of uterine activities related to preterm birth, but also to improved performance in classifying term/preterm pregnancies, i.e., the SVM (Support Vector Machine) classifier trained with the available samples in the TPEHG gives sensitivity, specificity, overall accuracy, and auc values as high as 0.89, 0.93, 0.91, and 0.97, respectively.

Continuous Decoding of Hand Movement From EEG Signals Using Phase-Based Connectivity Features.

The principal goal of the brain-computer interface (BCI) is to translate brain signals into meaningful commands to control external devices or neuroprostheses to restore lost functions of patients with severe motor disabilities. The invasive recording of brain signals involves numerous health issues. Therefore, BCIs based on non-invasive recording modalities such as electroencephalography (EEG) are safer and more comfortable for the patients. The BCI requires reconstructing continuous movement parameters such as position or velocity for practical application of neuroprostheses. The BCI studies in continuous decoding have extensively relied on extracting features from the amplitude of brain signals, whereas the brain connectivity features have rarely been explored. This study aims to investigate the feasibility of using phase-based connectivity features in decoding continuous hand movements from EEG signals. To this end, the EEG data were collected from seven healthy subjects performing a 2D center-out hand movement task in four orthogonal directions. The phase-locking value (PLV) and magnitude-squared coherence (MSC) are exploited as connectivity features along with multiple linear regression (MLR) for decoding hand positions. A brute-force search approach is employed to find the best channel pairs for extracting features related to hand movements. The results reveal that the regression models based on PLV and MSC features achieve the average Pearson correlations of 0.43 ± 0.03 and 0.42 ± 0.06, respectively, between predicted and actual trajectories over all subjects. The delta and alpha band features have the most contribution in regression analysis. The results also demonstrate that both PLV and MSC decoding models lead to superior results on our data compared to two recently proposed feature extraction methods solely based on the amplitude or phase of recording signals (p < 0.05). This study verifies the ability of PLV and MSC features in the continuous decoding of hand movements with linear regression. Thus, our findings suggest that extracting features based on brain connectivity can improve the accuracy of trajectory decoder BCIs.

ECG beat classification based on discriminative multilevel feature analysis and deep learning approach

Extraction of significant features from Electrocardiogram (ECG) signal is the primary concern for accurate diagnosis of cardiac arrhythmia. This work presents a novel approach of multilevel feature analysis and deep learning strategy for efficient ECG beat classification. The multilayer characteristics of ECG signal obtained from Empirical mode decomposition (EMD) are explored to extract discriminative feature vectors. The multilayer similarity coefficients are obtained by applying Dynamic time warping (DTW) metric and Pearson correlation coefficient (PCC) as diagnostic features. Furthermore, discrete orthonormal Stockwell transform (DOST) is employed for time–frequency representation of ECG data in multilayer aspect. The sublet changes in time–frequency spaces due to the presence of cardiac abnormalities are captured by estimating various nonlinear parameters. Interlayer deviations of these nonlinear parameters are estimated as the significant characteristics of arrhythmia detection. In addition, this study shows that the phase synchrony (PS) coefficients are prominent index for quantifying the crucial phase variation between normal and abnormal heart conditions. Hence multilevel PS coefficients are employed as the predictors of arrhythmia detection. Finally, the extracted feature vectors are fed to various classifiers to identify the heart anomalies. The proposed technique attains average accuracy of 98.82% and 98.14% using support vector machine (SVM) and k-nearest neighbors (k-NN) classifier respectively. The improved classification accuracy of 99.05% is obtained with the strategy of combining deep neural network (DNN) with the proposed feature extraction policy. Present work delivers satisfactory and superior performances for arrhythmia classification compare to other existing approaches.

UAT: Universal Attention Transformer for Video Captioning.

Video captioning via encoder–decoder structures is a successful sentence generation method. In addition, using various feature extraction networks for extracting multiple features to obtain multiple kinds of visual features in the encoding process is a standard method for improving model performance. Such feature extraction networks are weight-freezing states and are based on convolution neural networks (CNNs). However, these traditional feature extraction methods have some problems. First, when the feature extraction model is used in conjunction with freezing, additional learning of the feature extraction model is not possible by exploiting the backpropagation of the loss obtained from the video captioning training. Specifically, this blocks feature extraction models from learning more about spatial information. Second, the complexity of the model is further increased when multiple CNNs are used. Additionally, the author of Vision Transformers (ViTs) pointed out the inductive bias of CNN called the local receptive field. Therefore, we propose the full transformer structure that uses an end-to-end learning method for video captioning to overcome this problem. As a feature extraction model, we use a vision transformer (ViT) and propose feature extraction gates (FEGs) to enrich the input of the captioning model through that extraction model. Additionally, we design a universal encoder attraction (UEA) that uses all encoder layer outputs and performs self-attention on the outputs. The UEA is used to address the lack of information about the video’s temporal relationship because our method uses only the appearance feature. We will evaluate our model against several recent models on two benchmark datasets and show its competitive performance on MSRVTT/MSVD datasets. We show that the proposed model performed captioning using only a single feature, but in some cases, it was better than the others, which used several features.

PPG-based blood pressure estimation can benefit from scalable multi-scale fusion neural networks and multi-task learning

The continuous measurement of blood pressure (BP) plays an important role in preventing cardiovascular diseases. However, common cuff-based devices with cumbersome operations are difficult to realize continuous BP monitoring. We propose a continuous BP estimation method based on the end-to-end multi-scale fusion and multi-task learning neural network (MSF-MTLNet). It can use the 8-second PPG segment and its derivatives as input and automatically learn their multi-scale features to improve BP accuracy. The proposed method implements feature extraction by adaptively fusing multiple convolution kernels with different sizes through the attention mechanism. Simultaneously, multi-task learning was introduced to discover task-relevant features. A total of 277,600 segments from 1825 patients in the University of California, Irvine database are used for experiments. They are divided into the independent training set, validation set, and test set according to the ratio of 8:1:1 in an inter-subject manner. The mean and standard deviation of estimation error for systolic blood pressure and diastolic blood pressure are 0.97 ± 8.87 mmHg and 0.55 ± 4.23 mmHg, respectively, which outperforms the current cutting-edge method. Our approach almost meets the Association for the Advancement of Medical Instrumentation standard. Considering the British Hypertension Society standards, it achieves an 'A' grade for DBP estimation and a 'B' grade for SBP estimation. Experimental results show that MSF-MTLNet can obtain promising precision in cuffless and continuous BP estimation.

Development of Feature Extraction Technique

A problem of personal verification and identification is an actively growing area of research in pattern recognition, computer vision, and digital image processing. A Face acknowledgment system is largely significant move toward in our life. Face acknowledgment method must be proficient to robotically identify images of face. This system mainly used to recognize a person and make available a protection during various features of our life. It is extremely complicated job for investigator to obtain mainly excellent face acknowledgment velocity in a variety of circumstances and benchmarks. Face recognition is grassland of computer visualization to uses faces to recognize or authenticate a human being. Principal Component Analysis (PCA) is accomplished and used for feature extraction and measurement lessening. The feature extraction is used to reduce the dimension of the face space by transforming it into feature representation. Features may be symbolic, numerical or both. The symbolic feature is color and numerical feature is weight. The combined feature extraction of PCA, LDA and Wavelet are used in proposed feature extraction algorithm for human face recognition system. The structure is tested and achieves high recognition rates. Information regarding individuals was stored in a database.

Classification for Transient Overvoltages in Offshore Wind Farms Based on Sparse Decomposition

The transient overvoltages in offshore wind farms caused by faults or frequent operations of electrical equipment are particularly severe. In order to classify the internal transient overvoltages in offshore wind farms, this research firstly proposes a feature extraction method based on sparse decomposition using alternating direction method of multipliers, which extracts the harmonic and impulsive components from transient overvoltages. Then based on the impulsive components and transient overvoltages, two identification features are constructed. One is an impulsive feature and the other one is a ratio feature between impulsive energy values and original energy values. Finally, based on the constructed features, a support vector machine is employed to identify different types of internal transient overvoltages. Extensive simulations and experiments are performed to verify the effectiveness of the proposed feature extraction method, which is also compared with the widely used conventional wavelet algorithms and the mathematical morphology. Results show the proposed feature extraction method is capable of classifying and discriminating among various types of internal transient overvoltages with a strong self-adaptive ability and an improved accuracy.

1D Multi-Point Local Ternary Pattern: A Novel Feature Extraction Method for Analyzing Cognitive Engagement of students in Flipped Learning Pedagogy.

Flipped learning is a blended learning method based on academic engagement of students online (outside class) and offline (inside class). In this learning pedagogy, students receive lesson any time from lecture videos pre-loaded on digital platform at their convenience places and it is followed by in-classroom activities such as doubt clearing, problem solving, etc. However, students are constantly exposed to high levels of distraction in this age of the Internet. Therefore, it is hard for an instructor to know whether a student has paid attention while watching pre-loaded lecture video. In order to analyze attention level of individual students, captured brain signal or electroencephalogram (EEG) of students can be utilized. In this study, we utilize a popular feature extraction technique called Local Binary Pattern (LBP) and improvise it to develop an enhanced feature selection method. The adapted feature selection method termed as 1D Multi-Point Local Ternary Pattern (1D MP-LTP) is used to extract unique features from collected electroencephalogram (EEG) signals. Standard classification techniques are exploited to classify the attention level of students. Experiments are conducted with the data captured at Intelligent Data Analysis Lab, NIT Rourkela, to show effectiveness of the proposed feature extraction technique. The proposed 1D Multi-Point Local Ternary Pattern (1D MP-LTP)-based classification techniques outperform traditional and state-of-the-art classification techniques using LBP. This research can be helpful for instructors to identify students who need special care for improving their learning ability. Researchers in educational technology can extend this work by adopting this methodology in other online teaching pedagogy such as Massive Open Online Courses (MOOC).