| EEG signal analysis: a survey DP Subha, PK Joseph, R Acharya U, CM Lim Journal of medical systems 34, 195-212, 2010 | 750 | 2010 |
| Automated EEG-based screening of depression using deep convolutional neural network UR Acharya, SL Oh, Y Hagiwara, JH Tan, H Adeli, DP Subha Computer methods and programs in biomedicine 161, 103-113, 2018 | 567 | 2018 |
| A novel depression diagnosis index using nonlinear features in EEG signals UR Acharya, VK Sudarshan, H Adeli, J Santhosh, JEW Koh, ... European neurology 74 (1-2), 79-83, 2015 | 253 | 2015 |
| Automated Depression Detection Using Deep Representation and Sequence Learning with EEG Signals AUR Ay B, Yildirim O, Talo M, Baloglu UB, Aydin G, PuthankattilS D Journal of Medical Systems 43 (7), 205, 2019 | 235* | 2019 |
| Classification of EEG signals in normal and depression conditions by ANN using RWE and signal entropy S D.P., PK Joseph Journal of Mechanics in Medicine and Biology 12 (4), 1240019, 2012 | 129 | 2012 |
| Depression diagnosis support system based on EEG signal entropies O Faust, PCA Ang, SD Puthankattil, PK Joseph Journal of mechanics in medicine and biology 14 (03), 1450035, 2014 | 123 | 2014 |
| Prediction of depression from EEG signal using long short term memory (LSTM) SD Kumar, DP Subha 2019 3rd international conference on trends in electronics and informatics …, 2019 | 98 | 2019 |
| Performance analysis of deep learning CNN in classification of depression EEG signals P Sandheep, S Vineeth, M Poulose, DP Subha TENCON 2019-2019 IEEE Region 10 Conference (TENCON), 1339-1344, 2019 | 47 | 2019 |
| Complex network analysis of MCI-AD EEG signals under cognitive and resting state SDP Surya Das Brain Research 1735 (146743), 2020 | 46 | 2020 |
| Automated Diagnosis of Depression Electroencephalograph Signals Using Linear Prediction Coding and Higher Order Spectra Features GM 4. Bairy, OS Lih, Y Hagiwara, SD Puthankattil, O Faust, UC Niranjan, ... Journal of Medical Imaging and Health Informatics 7 (8), 1857-1862, 2017 | 45 | 2017 |
| EEG-based automated detection of schizophrenia using long short-term memory (LSTM) network A Nikhil Chandran, K Sreekumar, DP Subha Advances in Machine Learning and Computational Intelligence: Proceedings of …, 2021 | 39 | 2021 |
| Analysis of EEG signals using wavelet entropy and approximate entropy: A case study on depression patients SD Puthankattil, PK Joseph International Journal of Bioengineering and Life Sciences 8 (7), 430-434, 2014 | 37 | 2014 |
| Analysis of long range dependence in the EEG signals of Alzheimer patients TN John, SD Puthankattil, R Menon Cognitive Neurodynamics 12, 183-199, 2018 | 33 | 2018 |
| Automated classification of depression EEG signals using wavelet entropies and energies GM Bairy, UC Niranjan, SD Puthankattil Journal of Mechanics in Medicine and Biology 16 (03), 1650035, 2016 | 28 | 2016 |
| A hybrid method of artifact removal of visual evoked-EEG SDP 2. Priyalakshmi Sheela Journal of Neuroscience Methods 336, 108638, 2020 | 27 | 2020 |
| EEG signal processing: A survey DP Subha, KP Joseph, UR Acharya, CM Lim Journal of Medical Systems 34 (2), 195-212, 2010 | 22 | 2010 |
| Half-wave segment feature extraction of EEG signals of patients with depression and performance evaluation of Neural network Classifiers S D.P., PK Joseph Journal of Mechanics in Medicine and Biology 17 (2), 1750006, 2017 | 13 | 2017 |
| Exploration of time–frequency reassignment and homologous inter-hemispheric asymmetry analysis of MCI–AD brain activity PSDNRM T. Nimmy John BMC Neuroscience 20 (38), 38, 2019 | 10 | 2019 |
| Event related potential analysis techniques for autism spectrum disorders: A review P Sheela, SD Puthankattil International Journal of Developmental Neuroscience 68 (72-82), 2018 | 7 | 2018 |
| Automated detection and screening of depression using continuous wavelet transform with electroencephalogram signals U Raghavendra, A Gudigar, Y Chakole, P Kasula, DP Subha, NA Kadri, ... Expert Systems 40 (4), e12803, 2023 | 6 | 2023 |
