SREEJA S R

EDUCATION

B.Tech - Information Technology (2010), Anna University
ME- CSE (2014), Anna University
PhD - CSE (2020), IIT Kharagpur

RESEARCH INTERESTS

Artifact Removal from EEG Signals, Dictionary Learning, Sparse Approximation, Sparse Representation based Classification, Weighted Sparse Representation, Feature Extraction, Feature Optimization, Machine Learning.

Scopus Publications

TOPS: A Framework for Trusted Opinion Analysis of Product Reviews Using Hybrid Deep Learning Based D2CL Filter
T. K. Balaji, Annushree Bablani, S. R. Sreeja, and Hemant Misra
Wiley
ABSTRACTThe rapid growth of online product reviews has made it increasingly challenging for consumers to make informed purchase decisions. However, the abundance of reviews, including fake or augmented and sarcastic reviews, poses a challenge for consumers. To address this challenge, this paper introduces the TOPS (Trusted Opinion analysis of Product reviewS) framework, a novel approach that leverages a hybrid deep learning‐based D2CL (Dual Deep leaning based cleaning) filter to enhance the reliability of online reviews. The proposed methodology employs the D2CL filter to identify and eliminate fake and sarcastic reviews, ensuring that the consolidated sentiment analysis provides users with trustworthy opinions. The framework is equipped with the R‐mGRU, a hybrid deep learning model specifically designed to tackle the nuances of product reviews. This model has demonstrated impressive accuracy rates, achieving 89%, 91%, and 94% for fake, sarcasm, and sentiment analysis tasks, respectively. The TOPS framework makes a significant contribution to improving the overall quality and authenticity of product reviews, empowering consumers with more reliable information for informed decision‐making in online shopping scenarios.


SARCOVID: A Framework for Sarcasm Detection in Tweets Using Hybrid Transfer Learning Techniques
T. K. Balaji, Annushree Bablani, S. R. Sreeja, and Hemant Misra
Springer Nature Switzerland



2DP-FHS: 2D Pareto Optimized Fog Head Selection for Multiple EEG Healthcare Data Analysis and Computations
Sri Harsha Kurra, Rama Krushna Rath, and S. R. Sreeja
Springer Nature Switzerland



Sensecor: A framework for COVID-19 variants severity classification and symptoms detection
T. K. Balaji, Annushree Bablani, S. R. Sreeja, and Hemant Misra
Springer Science and Business Media LLC



Classification of Motor Imagery based EEG signals using Ensemble model
Saathvika Bandi, Venkata Sai Kasyap J, S. R. Sreeja, and Annushree Bablani
IEEE
The Brain-Computer Interfaces (BCIs) has been a source of fascination since its discovery. Controlling items just by thinking about them is a new degree of modernity. Out of many paradigms present in electroencephalogram (EEG), motor imagery (MI) has gained significance for being a safe and non-invasive process and including cognitive engagement. The main challenge for MI-based BCIs is Feature extraction and training a classifier, which could give us better accuracy. In our research, we adhere to the standard MI-based BCI workflow, but our methodology introduces an optimized and better model for classifying EEG data. To reduce the data used for the classification, we extracted five features, spanning both frequency and wavelet domain features, from the EEG signals. These features are then dimensionally reduced by passing through Linear Discriminant Analysis (LDA), and the best two features have been selected for classification. This Ensemble classifier model is applied to the best two features that have been extracted. This result is being compared with existing traditional Machine Learning algorithms - Support Vector Machines (SVM) and Logistic Regression (LR). Our empirical findings highlight the remarkable boost in classification accuracy achieved by the ensemble model compared to the tried-and-true machine learning methods. The suggested approach can be further enhanced to create a dependable and real-time BCI application.


Dictionary Learning and Greedy Algorithms for Removing Eye Blink Artifacts from EEG Signals
S. R. Sreeja, Shathanaa Rajmohan, Manjit Singh Sodhi, Debasis Samanta, and Pabitra Mitra
Springer Science and Business Media LLC



Dictionary reduction in sparse representation-based classification of motor imagery EEG signals
S. R. Sreeja and Debasis Samanta
Springer Science and Business Media LLC



Multi-cohort whale optimization with search space tightening for engineering optimization problems
Shathanaa Rajmohan, E. Elakkiya, and S. R. Sreeja
Springer Science and Business Media LLC



Classification of Motor Imagery based EEG Signals Using Deep Learning Architecture
Venkata Sai Kasyap J, Saathvika Bandi, Sreeja S R, and Santosh Kumar Satapathy
IEEE
Brain-computer interfaces (BCIs) aim to translate brain activity into control signals for external devices. Motor imagery (MI), the mental rehearsal of movements, has been studied extensively in BCIs as a means of decoding user intent from neural activity. Electroencephalogram (EEG) recordings provide a non-invasive interface for monitoring MI by detecting changes in brain activation. Accurately classifying MI from EEG signals is essential for developing practical MI-based BCIs. However, existing machine learning techniques require extensive preprocessing and handcrafted feature engineering, limiting their accuracy and efficiency. In this work, we propose a deep learning approach for end-to-end MI classification from raw EEG signals without manual feature extraction. Specifically, we employ a long short-term memory (LSTM) network to automatically learn spatiotemporal features from the EEG data. The extracted features are then fed into a multilayer perceptron (MLP) to perform classification. We demonstrate that our proposed framework achieves significantly higher accuracy in classifying right-hand and foot MI compared to conventional machine learning classifiers. Our results highlight the potential of deep learning to advance MI-based BCIs by learning highly complex representations of EEG signals in an end-to-end fashion. The proposed LSTM-MLP model thus represents a promising step towards more sophisticated BCIs that can translate raw neural data into control signals without the need for handcrafted features.


TSOSVNet: Teacher-student collaborative knowledge distillation for Online Signature Verification
Chandra Sekhar V, Avinash Gautam, Viswanath P, Sreeja Sr, and Rama Krishna Sai G
IEEE
Online signature verification (OSV) is a standardized personal authentication scheme with wide social acceptance in critical real-time applications include access control, m-commerce, etc. Even though the current advances in Deep learning (DL) technologies catalysed state-of-the-art frameworks for challenging domains like computer vision, speech recognition, etc., the DL-based frameworks are voluminous with huge trainable parameters and are hard to deploy in real-time systems demanding faster inference. To adopt DL into OSV for improved performance, we propose an OSV framework made up of teacher-student collaborative knowledge distillation (TSKD) technique. A heavy Transformer based teacher is trained first and the teacher knowledge is distilled into a very lightweight Convolutional Neural Network (CNN) based student. A well trained teacher network results in an efficient deep representative feature learning by the student and results in a performance improvement. In a thorough set of experiments with three popular and standard datasets, i.e., the MCYT-100, SUSIG, and SVC, TSOSVNet framework, with a CNN based student model requiring only 3266 trainable parameters results in an EER of 12.42% compared to the recent SOTA 13.38% by a model with 206277 parameters in skilled_01 category of MCYT-100 dataset. In comparison to cutting-edge CNN-based OSV models, the proposed TSOSVNet produced a state-of-the-art EER in the most of the test categories with an average of 90% lesser trainable parameters.


MS3A: Wrapper-Based Feature Selection with Multi-swarm Salp Search Optimization
Rajmohan Shathanaa, S. R. Sreeja, and E. Elakkiya
Springer Nature Singapore



Moment Centralization-Based Gradient Descent Optimizers for Convolutional Neural Networks
Sumanth Sadu, Shiv Ram Dubey, and S. R. Sreeja
Springer Nature Singapore



A Deep Learning Approach to Automated Sleep Stages Classification Using Multi-Modal Signals
Santosh Kumar Satapathy, Hari Kishan Kondaveeti, S R Sreeja, Hiral Madhani, Nitinsingh Rajput, and Debabrata Swain
Elsevier BV



An Automated System for Sleep Staging using EEG Brain Signals Based on A Machine Learning Approach
Santosh Kumar Satapathy, Hari Kishan Kondaveeti, and S R Sreeja
IEEE
Correctly classifying the sleep stages is essential for analyzing sleep quality and diagnosing sleep disorders. This research article aims to explore the classification performance of state-of-the-art machine learning (ML) models for classifying the sleep stages of different health-conditioned subjects and to identify whether the model performed well consistently for all kinds of sleep-disordered data. However, it has been noticed that the existing sleep staging methods have the following limitations:(1) Manual inspection of sleep behavior requires experts’ knowledge and which requires more workforce and a time-consuming process (2) Due to similar characteristics of the sleep stages features, it is essential to discriminate the changes aspects of the individual sleep stages with proper selection of parts (3) Acquisition of different medical-conditioned data has high demands on equipment. To address these challenges, we proposed an ML-based automated sleep staging system to analyze and classify sleep patterns. We use three different subgroups, namely SG-I, SG-II, and SG-III, to validate the proposed model’s effectiveness. First, our proposed model considered three different categories of sleep data with varying recordings of sessions and then extracted various features to understand the sleep behavior and applied feature selection algorithms to validate the feature concerning different subgroups’ sleep dates. Finally, apply the support vector machine (SVM) algorithm to classify them as Wake vs. Sleep on different sub-groups data ISRUC-Sleep dataset. The experimental results of the proposed method on the public repository ISRUC-Sleep show that it is improved. The results signify that the proposed method performs better than a state-of-the-art method.


Development of Efficient Ensemble Model based on Stacking Learning for Automated Sleep Staging
Santosh Kumar Satapathy, Hari Kishan Kondaveeti, S R Sreeja, and Hiral Madhani
IEEE
Sleep plays a vital role in maintaining good physical and mental health. Improper sleep causes many critical diseases; sleep-related disorders are significant global challenges. The primary diagnosis step for such disorders is a classification of sleep stages. Compared to traditional complex manual sleep analysis, automated sleep staging methods using single-channel electroencephalography (EEG) signals more practical benefits for analyzing the changes in characteristics in different sleep stages. Many of the existing contributions are based on computer-aided techniques for this task; among these, some of these classification models performed well to improve the classification accuracy results, but the achieved accuracy has not been reached for clinical applications due to improper selection of features. In this work, we consider an effective ensemble learning algorithm for improving the classification accuracy of sleep stages. Previous research works have not performed satisfactory classification performance, ignored some compelling features, and not identified suitable ones. This work extracts different feature extraction to obtain valuable features from EEG signals. Meanwhile, an effective classification process based on ensemble learning is proposed to improve the classification results automatically. The proposed model evaluated eight subjects of the ISRUC-Sleep public dataset, and the model reported accuracy of 97.86% using subgroup-I and 99.02% using sub-group-III data, respectively. Experimental results show that the classification performance of the proposed method is incomparably enhanced compared to the state-of-the-art works. This sleep stage classification framework is expected to assist the medical professional in diagnosing the different types of sleep-related disorders.


Opinion mining on COVID-19 vaccines in India using deep and machine learning approaches
Balaji T.K., Annushree Bablani, and Sreeja SR
IEEE
Since the COVID-19 outbreak, considering the people’s opinion has been perceived as the most crucial challenge for the government to combat the pandemic, such as implementing a national lockdown, instituting a quarantine procedure, providing health services, and more. Furthermore, the government made many critical decisions based on public opinion to combat coronavirus. Opinion mining or sentiment analysis has arisen as a method for mining people’s views on several issues using machine learning techniques. With the support of machine learning methods, this paper extracted the Indian people’s opinions on vaccines through Twitter tweets. More than four lakh vaccine-related tweets from May 04 to May 11, 2021, and from Aug 13 to Aug 21, 2021, were analyzed using state-of-the-art machine learning and deep learning approaches. The BERT and RoBERTa models produced promising results compared to other models on the collected twitter dataset.


Emotion Recognition from Brain Signals While Subjected to Music Videos
Puneeth Yashasvi Kashyap Apparasu and S. R. Sreeja
Springer International Publishing



Distance-based weighted sparse representation to classify motor imagery EEG signals for BCI applications
S. R. Sreeja, Himanshu, and Debasis Samanta
Springer Science and Business Media LLC
Motor imagery (MI) based brain-computer interface systems (BCIs) are highly in need for a large number of real-time applications such as hands and touch-free text entry system, movement of a wheelchair, movement of a cursor, prosthetic arm movement, virtual reality systems, etc. In recent years, sparse representation-based classification (SRC) is a growing technique and has been a successful technique on classifying MI-based Electroencephalography (EEG) signals. To further boost the proficiency of SRC technique, in this paper, a weighted SRC (WSRC) has been proposed for classifying MI signals. In WSRC approach, a weighted dictionary has been constructed according to the dissimilarity information between a test data and training samples. Then for the given test data, the sparse coefficients are computed over the weighted dictionary using l 0 -minimization problem. The sparse solution obtained using WSRC gives discriminative information and as a consequence, WSRC proves to be superior for MI-based EEG classification. The experimental results substantiate that WSRC is more efficient and accurate than SRC.


Classification of multiclass motor imagery EEG signal using sparsity approach
S. R. Sreeja and Debasis Samanta
Elsevier BV
Abstract Motor imagery (MI) based brain–computer interface systems involving multiple tasks are highly required in many real-time applications such as hands and touch-free text entry, prosthetic arms, virtual reality systems, movement of a wheel chair, cursor movement, etc. The classification of MI data is the core computing in all these systems. However, the existing classification techniques are either computationally expensive or not so accurate or both. To address this limitation, in this work, a sparse representation based classification technique has been proposed to classify multi-tasks MI electroencephalogram data. The proposed method computes only wavelet energy directly from the segmented MI data and constructs a dictionary. The sparse representation from the dictionary is then used to classify given a test data. The proposed approach is faster as it works with only a single feature and without the need for any pre-processing. Further, with a reduced length of an imaging period, the proposed method provides accurate classification in a lesser computation time. The performance of the proposed approach has been evaluated and also compared with other classifiers reported in the literature. The results substantiate that the proposed sparsity approach performs significantly better than the existing classifiers.


Weighted sparse representation for classification of motor imagery EEG signals
S. R. Sreeja, Himanshu, Debasis Samanta, and Monalisa Sarma
IEEE
Motor imagery (MI) based brain-computer interface systems (BCIs) are highly in demand for many real-time applications such as hands and touch-free text entry, prosthetic arms, virtual reality, movement of wheelchairs, etc. Traditional sparse representation based classification (SRC) is a thriving technique in recent years and has been a successful approach for classifying MI EEG signals. To further improve the capability of SRC, in this paper, a weighted SRC (WSRC) has been proposed for classifying two-class MI tasks (right-hand, right-foot). WSRC constructs a weighted dictionary according to the dissimilarity information between the test data and the training samples. Then for the given test data the sparse coefficients are computed over the weighted dictionary using l0-minimization problem. The sparse solution obtained using WSRC gives better discriminative information than SRC and as a consequence, WSRC proves to be superior for MI EEG classification. The experimental results substantiate that WSRC is more efficient and accurate than SRC.


Removal of Eye Blink Artifacts from EEG Signals Using Sparsity
S. R. Sreeja, Rajiv Ranjan Sahay, Debasis Samanta, and Pabitra Mitra
Institute of Electrical and Electronics Engineers (IEEE)
Neural activities recorded using electroencephalography (EEG) are mostly contaminated with eye blink (EB) artifact. This results in undesired activation of brain–computer interface (BCI) systems. Hence, removal of EB artifact is an important issue in EEG signal analysis. Of late, several artifact removal methods have been reported in the literature and they are based on independent component analysis (ICA), thresholding, wavelet transformation, etc. These methods are computationally expensive and result in information loss which makes them unsuitable for online BCI system development. To address the above problems, we have investigated sparsity-based EB artifact removal methods. Two sparsity-based techniques namely morphological component analysis (MCA) and K-SVD-based artifact removal method have been evaluated in our work. MCA-based algorithm exploits the morphological characteristics of EEG and EB using predefined Dirac and discrete cosine transform (DCT) dictionaries. Next, in K-SVD-based algorithm an overcomplete dictionary is learned from the EEG data itself and is designed to model EB characteristics. To substantiate the efficacy of the two algorithms, we have carried out our experiments with both synthetic and real EEG data. We observe that the K-SVD algorithm, which uses a learned dictionary, delivers superior performance for suppressing EB artifacts when compared to MCA technique. Finally, the results of both the techniques are compared with the recent state-of-the-art FORCe method. We demonstrate that the proposed sparsity-based algorithms perform equal to the state-of-the-art technique. It is shown that without using any computationally expensive algorithms, only with the use of over-complete dictionaries the proposed sparsity-based algorithms eliminate EB artifacts accurately from the EEG signals.


Classification of EEG signals for cognitive load estimation using deep learning architectures
Anushri Saha, Vikash Minz, Sanjith Bonela, S. R. Sreeja, Ritwika Chowdhury, and Debasis Samanta
Springer International Publishing
Measuring cognitive load is crucial for many applications such as information personalization, adaptive intelligent tutoring systems, etc. Cognitive load estimation using Electroencephalogram (EEG) signals is widespread as it produces clear indications of cognitive activities by measuring changes of neural activation in the brain. However, the existing cognitive load estimation techniques are based on machine learning algorithms, which follow signal denoising and hand-crafted feature extraction to classify different loads. There is a need to find a better alternative to the machine learning approach. Of late, deep learning approach has been successfully applied to many applications namely, computer vision, pattern recognition, speech processing, etc. However, deep learning has not been extensively studied for the classification of cognitive load data captured by an EEG. In this work, two deep learning models are studied, namely stacked denoising autoencoder (SDAE) followed by a multilayer perceptron (MLP) and long short term memory (LSTM) followed by an MLP to classify cognitive load data. SDAE and LSTM are used for feature extraction and MLP for classification. It is observed that deep learning models perform significantly better than the conventional machine learning classifiers such as support vector machine (SVM), k-nearest neighbors (KNN), and linear discriminant analysis (LDA).


Motor Imagery EEG Signal Processing and Classification Using Machine Learning Approach
S.R. Sreeja, Joytirmoy Rabha, K.Y. Nagarjuna, Debasis Samanta, Pabitra Mitra, and Monalisa Sarma
IEEE
Motor imagery (MI) signals recorded via electroencephalography (EEG) is the most convenient basis for designing brain-computer interfaces (BCIs). As MI based BCI provides high degree of freedom, it helps motor disabled people to communicate with the device by performing sequence of MI tasks. But inter-subject variability, extracting user-specific features and increasing accuracy of the classifier is still a challenging task in MI based BCIs. In this work, we propose an approach to overcome the above mentioned issues. The proposed approach follows the pipeline such as channel selection, band-pass filter based CSP (common spatial pattern), feature extraction, feature selection using two different techniques and modeling using Gaussian Naïve Bayes (GNB) classifier. Since the optimal features are selected by feature selection techniques, it helps to overcome inter-subject variability and improves performance of GNB classifier. To the best of our knowledge, the proposed methodology has not been used for MI-based BCI applications. The proposed approach is validated using BCI competition III dataset IVa. The result of our proposed approach is compared with two conventional classifiers such as linear discriminant analysis (LDA) and support vector machine (SVM). The results prove that the proposed method provides an improved accuracy than LDA and SVM classifiers. The proposed method can be further developed to design a reliable and real-time MI-based BCI application.


Classification of motor imagery based EEG signals using sparsity approach
S. R. Sreeja, Joytirmoy Rabha, Debasis Samanta, Pabitra Mitra, and Monalisa Sarma
Springer International Publishing
The advancement in brain-computer interface systems (BCIs) gives a new hope to people with special needs in restoring their independence. Since, BCIs using motor imagery (MI) rhythms provides high degree of freedom, it is been used for many real-time applications, especially for locked-in people. The available BCIs using MI-based EEG signals usually makes use of spatial filtering and powerful classification methods to attain better accuracy and performance. Inter-subject variability and speed of the classifier is still a issue in MI-based BCIs. To address the aforementioned issues, in this work, we propose a new classification method, spatial filtering based sparsity (SFS) approach for MI-based BCIs. The proposed method makes use of common spatial pattern (CSP) to spatially filter the MI signals. Then frequency bandpower and wavelet features from the spatially filtered signals are used to bulid two different over-complete dictionary matrix. This dictionary matrix helps to overcome the issue of inter-subject variability. Later, sparse representation based classification is carried out to classify the two-class MI signals. We analysed the performance of the proposed approach using publicly available MI dataset IVa from BCI competition III. The proposed SFS method provides better classification accuracy and runtime than the well-known support vector machine (SVM) and logistic regression (LR) classification methods. This SFS method can be further used to develop a real-time application for people with special needs.


BCI Augmented text entry mechanism for people with special needs
Sreeja S.R., Vaidic Joshi, Shabnam Samima, Anushri Saha, Joytirmoy Rabha, Baljeet Singh Cheema, Debasis Samanta, and Pabitra Mitra
Springer International Publishing
The ability to feel, adapt, reason, remember and communicate makes human a social being. Disabilities limit opportunities and capabilities to socialize. With the recent advancement in brain-computer interface (BCI) technology, researchers are exploring if BCI can be augmented with human computer interaction (HCI) to give a new hope of restoring independence to disabled individuals. This motivates us to lay down our research objective, which is as follows. In this study, we propose to work with a hands-free text entry application based on the brain signals, for the task of communication, where the user can select a letter or word based on the intentions of left or right hand movement. The two major challenges that have been addressed are (i) interacting with only two imagery signals (ii) how a low-quality, noisy EEG signal can be competently processed and classified using novel combination of feature set to make the interface work efficiently. The results of five able-bodied users show that the error rate per minute is significantly reduced and it also illustrates that it can be further used to develop better BCI augmented HCI systems.

RECENT SCHOLAR PUBLICATIONS

TOPS: A Framework for Trusted Opinion Analysis of Product Reviews Using Hybrid Deep Learning Based D2CL Filter
TK Balaji, A Bablani, SR Sreeja, H Misra
Expert Systems 42 (2), e13765 2025



SARCOVID: A Framework for Sarcasm Detection in Tweets Using Hybrid Transfer Learning Techniques
TK Balaji, A Bablani, SR Sreeja, H Misra
International Conference on Pattern Recognition, 1-12 2025



Sentiment and sarcasm: Analyzing gender bias in sports through social media with deep learning
S Praveen, B TK, Sreeja SR, A Bablani
ICON 2024, 132-138 2024



SASE: Sentiment Analysis with Aspect Specific Evaluation Using Deep Learning with Hybrid Contextual Embedding
TK Balaji, A Bablani, SR Sreeja, H Misra
20th International Conference on Distributed Computing and Intelligent 2024



2DP-FHS: 2D Pareto Optimized Fog Head Selection for Multiple EEG Healthcare Data Analysis and Computations
SH Kurra, RK Rath, SR Sreeja
International Conference on Advances in Computing and Data Sciences, 58-68 2024



MINDSCOPE: Machine-learning Inferencing of NeuroData for Seamless Cognitive Overload Prediction and Evaluation
R Katinni, SR Sreeja, A Bablani
2024



Classification of Motor Imagery based EEG signals using Ensemble model
S Bandi, SR Sreeja, A Bablani
2024 3rd International Conference for Innovation in Technology (INOCON), 1-6 2024



Classification of Motor Imagery based EEG Signals Using Deep Learning Architecture
V Sai Kasyap J, S Bandi, SR Sreeja, SK Satapathy
2023 IEEE 20th India Council International Conference (INDICON), 806-811 2024



Sensecor: A framework for COVID-19 variants severity classification and symptoms detection
TK Balaji, A Bablani, SR Sreeja, H Misra
Evolving Systems 15 (1), 65-82 2024



TSOSVNet: Teacher-student collaborative knowledge distillation for Online Signature Verification
CS V, A Gautam, V P, SR Sreeja, RKS G
Proceedings of the IEEE/CVF International Conference on Computer Vision 2023



MS3A: Wrapper-Based Feature Selection with Multi-swarm Salp Search Optimization
R Shathanaa, SR Sreeja, E Elakkiya
Advances in Data-driven Computing and Intelligent Systems: Selected Papers 2023



Moment Centralization based Gradient Descent Optimizers for Convolutional Neural Networks
S Sadu, SR Dubey, SR Sreeja
Computer Vision and Machine Intelligence 586, 51 - 63 2023



Dictionary Learning and Greedy Algorithms for removing Eye Blink Artifacts from EEG Signals
SR Sreeja, S Rajmohan, MS Sodhi, D Samanta, P Mitra
Circuits, Systems, and Signal Processing 2023



Multi-cohort whale optimization with search space tightening for engineering optimization problems
S Rajmohan, E Elakkiya, SR Sreeja
Neural Computing and Applications 35 (12), 8967-8986 2023



Dictionary Reduction in Sparse Representation-based Classification of Motor Imagery EEG Signals
SR Sreeja, D Samanta
Multimedia Tools and Applications 2023



A deep learning approach to automated sleep stages classification using multi-modal signals
SK Satapathy, HK Kondaveeti, SR Sreeja, H Madhani, N Rajput, D Swain
Procedia Computer Science 218, 867-876 2023



Development of Efficient Ensemble Model based on Stacking Learning for Automated Sleep Staging
S S. K, K H. K, SR Sreeja
2022 International Conference on Innovation and Intelligence for Informatics 2022



An automated system for sleep staging using EEG brain signals based on a machine learning approach
SK Satapathy, HK Kondaveeti, SR Sreeja
IEEE INDICON 2022, 1-6 2022



Opinion mining on COVID-19 vaccines in India using deep and machine learning approaches
TK Balaji, A Bablani, SR Sreeja
2022 International Conference on Innovative Trends in Information Technology 2022



Emotion recognition from brain signals while subjected to music videos
PYK Apparasu, SR Sreeja
International Conference on Intelligent Human Computer Interaction, 772-782 2021

MOST CITED SCHOLAR PUBLICATIONS

Removal of Eye Blink Artifacts from EEG Signals using Sparsity
SR Sreeja, RR Sahay, D Samanta, P Mitra
IEEE Journal of Biomedical and Health Informatics 22 (5), 1362 - 1372 2017
Citations: 73



Motor Imagery EEG Signal Processing and Classification using Machine Learning Approach
SR Sreeja, D Samanta, P Mitra, M Sarma
Jordanian Journal of Computers and Information Technology (JJCIT) 4 (02), 80-93 2018
Citations: 62



Motor imagery EEG signal processing and classification using machine learning approach
Sreeja SR, J Rabha, KY Nagarjuna, D Samanta, P Mitra, M Sarma
2017 International Conference on New Trends in Computing Sciences (ICTCS), 61-66 2017
Citations: al processing and classification using machine learning approach



Classification of multiclass motor imagery EEG signal using sparsity approach
SR Sreeja, D Samanta
Neurocomputing 368, 133-145 2019
Citations: 48



Classification of motor imagery based EEG signals using sparsity approach
SR Sreeja, J Rabha, D Samanta, P Mitra, M Sarma
Intelligent Human Computer Interaction: 9th International Conference, IHCI 2017
Citations: 40



Classification of EEG signals for cognitive load estimation using deep learning architectures
A Saha, V Minz, S Bonela, SR Sreeja, R Chowdhury, D Samanta
Intelligent Human Computer Interaction: 10th International Conference, IHCI 2018
Citations: 37



Distance-based weighted sparse representation to classify motor imagery EEG signals for BCI applications
SR Sreeja, Himanshu, D Samanta
Multimedia Tools and Applications 2020
Citations: 34



Multi-cohort whale optimization with search space tightening for engineering optimization problems
S Rajmohan, E Elakkiya, SR Sreeja
Neural Computing and Applications 35 (12), 8967-8986 2023
Citations: 15



BCI augmented text entry mechanism for people with special needs
SR Sreeja, V Joshi, S Samima, A Saha, J Rabha, BS Cheema, ...
Intelligent Human Computer Interaction: 8th International Conference, IHCI 2017
Citations: 15



A deep learning approach to automated sleep stages classification using multi-modal signals
SK Satapathy, HK Kondaveeti, SR Sreeja, H Madhani, N Rajput, D Swain
Procedia Computer Science 218, 867-876 2023
Citations: 14



Weighted sparse representation for classification of motor imagery EEG signals
SR Sreeja, Himanshu, D Samanta, M Sarma
2019 41st Annual International Conference of the IEEE Engineering in 2019
Citations: 8



An automated approach for task evaluation using EEG signals
V Anand, SR Sreeja, D Samanta
arXiv preprint arXiv:1911.02966 2019
Citations: 5



An automated system for sleep staging using EEG brain signals based on a machine learning approach
SK Satapathy, HK Kondaveeti, SR Sreeja
IEEE INDICON 2022, 1-6 2022
Citations: 4



Opinion mining on COVID-19 vaccines in India using deep and machine learning approaches
TK Balaji, A Bablani, SR Sreeja
2022 International Conference on Innovative Trends in Information Technology 2022
Citations: 4



Dictionary Learning and Greedy Algorithms for removing Eye Blink Artifacts from EEG Signals
SR Sreeja, S Rajmohan, MS Sodhi, D Samanta, P Mitra
Circuits, Systems, and Signal Processing 2023
Citations: 3



Emotion recognition from brain signals while subjected to music videos
PYK Apparasu, SR Sreeja
International Conference on Intelligent Human Computer Interaction, 772-782 2021
Citations: 3



Dictionary Reduction in Sparse Representation-based Classification of Motor Imagery EEG Signals
SR Sreeja, D Samanta
Multimedia Tools and Applications 2023
Citations: 2



Development of Efficient Ensemble Model based on Stacking Learning for Automated Sleep Staging
S S. K, K H. K, SR Sreeja
2022 International Conference on Innovation and Intelligence for Informatics 2022
Citations: 2



Classification of Motor Imagery based EEG signals using Ensemble model
S Bandi, SR Sreeja, A Bablani
2024 3rd International Conference for Innovation in Technology (INOCON), 1-6 2024
Citations: 1



Sensecor: A framework for COVID-19 variants severity classification and symptoms detection
TK Balaji, A Bablani, SR Sreeja, H Misra
Evolving Systems 15 (1), 65-82 2024
Citations: 1