Table 4.
The top 10 co-cited references in the field of EEG research related to depression.
| Rank | Co-cited references | Citations | Journal | Types |
|---|---|---|---|---|
| 1 | EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis Delorme A, 2004, J. Neurosci. Methods, V134, P9, DOI: 10.1016/J.JNEUMETH.2003.10.009 | 103 | Journal of Neuroscience Methods | Article |
| 2 | Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal Hosseinifard B, 2013, Comput. Methods Programs Biomed., V109, P339, DOI: 10.1016/J.CMPB.2012.10.008 | 92 | Computer Methods and Programs In Biomedicine | Article |
| 3 | Automated EEG-based screening of depression using deep convolutional neural network Acharya UR, 2018, Comput. Methods Programs Biomed, V161, P103, DOI: 10.1016/J.CMPB.2018.04.012 | 77 | Computer Methods and Programs in Biomedicine | Article |
| 4 | EEG power, frequency, asymmetry and coherence in male depression Knott V, 2001, Psychiatry Res., V106, P123, DOI: 10.1016/S0925-4927(00)00080-9 | 76 | Psychiatry Research-Neuroimaging | Article |
| 5 | Left frontal hypoactivation in depression Henriques JB, 1991, J. Abnorm. Psychol., V100, P535, DOI: 10.1037/0021-843X.100.4.535 | 72 | Journal of Abnormal Psychology | Article |
| 6 | A rating scale for depression Hamilton M, 1960, J. Neurol. Neurosurg. Psychiatry, V23, P56, DOI: 10.1136/JNNP.23.1.56 | 70 | Journal of Neurology Neurosurgery and Psychiatry | Article |
| 7 | Depression biomarkers using non-invasive EEG: a review Neto FSD, 2019, Neurosci. Biobehav. Rev., V105, P83, DOI: 10.1016/J.NEUBIOREV.2019.07.021 | 67 | Neuroscience and Biobehavioral Reviews | Review |
| 8 | EEG biomarkers in major depressive disorder: discriminative power and prediction of treatment response Olbrich S, 2013, Int. Rev. Psychiatry, V25, P604, DOI: 10.3109/09540261.2013.816269 | 67 | International Review of Psychiatry | Article |
| 9 | A novel depression diagnosis index using nonlinear features in EEG signals Acharya UR, 2015, Eur. Neurol., V74, P79, DOI: 10.1159/000438457 | 56 | European Neurology | Article |
| 10 | A pervasive approach to EEG-based depression detection Cai HS, 2018, Complexity, V0, P0, DOI: 10.1155/2018/5238028 | 55 | Complexity | Article |