Enhancing EEG-Based Classification of Depression Patients Using Spatial Information

Jiang, Chao; Li, Yingjie; Tang, Yingying; Guan, Cuntai

doi:10.1109/tnsre.2021.3059429

Cited by 72 publications

(43 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Previous studies [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ] had a very limited number of participants (see Figure 1 for detail). Therefore, it is difficult to partition the available EEG dataset of small sample size into a training dataset used for training classifiers and an independent test dataset of sufficient size.…”

Section: Cross Validation Used In Previous Work: More Detailed Review and Analysismentioning

confidence: 99%

“…To evaluate the participant-independent classification performance, a few studies used the leave-one-participant-out CV (LOPO-CV) strategy [ 20 , 21 , 24 ]. In each iteration of the LOPO-CV procedure, the EEG data of one participant were used for testing, while the data of remaining participants were used as the training set.…”

Section: Cross Validation Used In Previous Work: More Detailed Review and Analysismentioning

confidence: 99%

“…Many studies have tried to apply machine learning (ML) methods to classify resting-state (e.g., [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ]) versus task-related (e.g., [ 21 , 24 , 25 ]) electroencephalography (EEG) signals between MDD and healthy control (HC) groups. A few studies have also proposed multi-model systems that fuse EEG signals and other physiological data (e.g., [ 26 ]).…”

Section: Introductionmentioning

confidence: 99%

“…The sample sizes in these ML-based studies [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ] were all less than 100 (24~90), except for two studies by Cai et al [ 12 , 15 ] in which the subject numbers were 267 and 213, respectively (however, they only measured a very low-density EEGs from three electrodes at FP1, FP2, and Fz). Previous studies, due to limited data size, have only performed cross validation procedures on limited EEG data to estimate the generalization performance of their proposed algorithms/models, without using an independent test set of sufficiently large size.…”

Section: Introductionmentioning

confidence: 99%

“…These inevitable factors of EEG variability have been considered to be a challenge to real-world brain-computer interface (BCI) [ 29 , 30 ] and EEG-based applications (e.g., seizure detection [ 31 ]). In this view, most of the previous EEG studies [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ] have lacked a validation on datasets with high diversity (or large EEG variability) because their data were all collected from a single site.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Resting-State EEG Signal for Major Depressive Disorder Detection: A Systematic Validation on a Large and Diverse Dataset

Huang²,

Huang³

et al. 2021

Biosensors

View full text Add to dashboard Cite

Major depressive disorder (MDD) is a global healthcare issue and one of the leading causes of disability. Machine learning combined with non-invasive electroencephalography (EEG) has recently been shown to have the potential to diagnose MDD. However, most of these studies analyzed small samples of participants recruited from a single source, raising serious concerns about the generalizability of these results in clinical practice. Thus, it has become critical to re-evaluate the efficacy of various common EEG features for MDD detection across large and diverse datasets. To address this issue, we collected resting-state EEG data from 400 participants across four medical centers and tested classification performance of four common EEG features: band power (BP), coherence, Higuchi’s fractal dimension, and Katz’s fractal dimension. Then, a sequential backward selection (SBS) method was used to determine the optimal subset. To overcome the large data variability due to an increased data size and multi-site EEG recordings, we introduced the conformal kernel (CK) transformation to further improve the MDD as compared with the healthy control (HC) classification performance of support vector machine (SVM). The results show that (1) coherence features account for 98% of the optimal feature subset; (2) the CK-SVM outperforms other classifiers such as K-nearest neighbors (K-NN), linear discriminant analysis (LDA), and SVM; (3) the combination of the optimal feature subset and CK-SVM achieves a high five-fold cross-validation accuracy of 91.07% on the training set (140 MDD and 140 HC) and 84.16% on the independent test set (60 MDD and 60 HC). The current results suggest that the coherence-based connectivity is a more reliable feature for achieving high and generalizable MDD detection performance in real-life clinical practice.

show abstract

Section: Cross Validation Used In Previous Work: More Detailed Review and Analysismentioning

confidence: 99%

Section: Cross Validation Used In Previous Work: More Detailed Review and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Resting-State EEG Signal for Major Depressive Disorder Detection: A Systematic Validation on a Large and Diverse Dataset

Huang²,

Huang³

et al. 2021

Biosensors

View full text Add to dashboard Cite

show abstract

The Role of EEG as Neuro-Markers for Patients with Depression: A Systematic Review

Al-Qazzaz

Aldoori

2023

Advances in Non-Invasive Biomedical Signal Sensing and Processing With Machine Learning

View full text Add to dashboard Cite

Die Rolle des EEG als Neuro-Marker für Patienten mit Depression: Ein systematischer Überblick

Al-Qazzaz,

Aldoori

2024

Fortschritte in Der Nicht-Invasiven Biomedizinischen Signalverarbeitung Mit ML

View full text Add to dashboard Cite

Enhancing EEG-Based Classification of Depression Patients Using Spatial Information

Cited by 72 publications

References 47 publications

Resting-State EEG Signal for Major Depressive Disorder Detection: A Systematic Validation on a Large and Diverse Dataset

Resting-State EEG Signal for Major Depressive Disorder Detection: A Systematic Validation on a Large and Diverse Dataset

The Role of EEG as Neuro-Markers for Patients with Depression: A Systematic Review

Die Rolle des EEG als Neuro-Marker für Patienten mit Depression: Ein systematischer Überblick

Contact Info

Product

Resources

About