Abstract

In multichannel EEG, several electrodes are attached to the head that may be annoying for patients and troublesome for operators. Moreover, the number of electrodes is the main reason of the infeasibility of developing EEG based wearable and point of care devices. To address this problem, recently, the concept of single-channel EEG (SCEEG) is presented. The spatial resolution of SCEEG is lower than the multichannel one, but it is easy to use, cost-effective, ubiquitous, and wearable. In this paper, for the first time, we have developed the concept of SCEEG for the classification of responders and nonresponders to repetitive transcranial magnetic stimulation (rTMS) treatment in major depressive disorder (MDD). We also compared the performance of SCEEG and multichannel EEG with the different number of channels in the prediction of responding to rTMS treatment. 19-electrode EEG is recorded from 46 MDD patients before rTMS treatment. Among participants, 23 individuals responded to treatment. The dataset is partitioned into the training (36 subjects) and testing (10 subjects) datasets. Linear and nonlinear features were extracted from every channel of EEG. In training, to select informative features, the minimal-redundancy-maximal-relevance (mRMR) algorithm was applied. The selected features were classified by k-nearest neighbors (KNN) classifier, which is evaluated by leave-one-out cross-validation. Then the obtained classifier is applied to the testing dataset. The results demonstrated that the F8 channel classifies responders and nonresponders with an accuracy of 80%. Moreover, our results revealed that SCEEG could perform as multichannel EEG in the prediction of rTMS treatment outcome in MDD patients. The obtained accuracy indicates that our proposed method based on SCEEG has a high potential for predicting rTMS treatment outcome in MDD patients.

Highlights

  • EEG is a non-invasive method for monitoring brain activity and has various applications in the detection and treatment of brain disorders

  • It can be seen that the highest accuracy of 80% in the testing dataset is obtained by classification based on 17 to 3 channels and single channel

  • For the first time in this paper, we proposed a framework based on single channel EEG for predicting treatment outcome of repetitive transcranial magnetic stimulation (rTMS) in major depressive disorder (MDD) patients

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Summary

Introduction

EEG is a non-invasive method for monitoring brain activity and has various applications in the detection and treatment of brain disorders. To use multichannel EEG, several gel-impregnated electrodes should be attached to the head of the patients. These electrodes are connected to some electrical devices (e.g., amplifiers) with many wires. In some other applications like neurofeedback training, it is necessary to have frequent recordings that are another critical constraint for multichannel EEGs. Because it is tough to keep lots of electrodes on the head of subjects for several trials [3]. Because it is tough to keep lots of electrodes on the head of subjects for several trials [3] To address these issues, portable EEG devices that have fewer electrodes

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