## 1 Introduction  
Major depressive disorder (MDD) is characterized by persistent sadness, hopelessness, and inability to feel pleasure in normally enjoyable activities (i.e., anhedonia [1]). MDD is also associated with deficits in executive function [2] and memory [3], and recent depression is a risk factor for suicide [4]. Because depression is prevalent and recurrent, the World Health Organization (WHO) ranks it as a leading contributor to the global burden of disease [5]. To effectively treat MDD, a safe, objective, and convenient method for accurate diagnosis is essential. Electroencephalography (EEG) is increasingly recognized as a promising tool in this regard [6], because it is a well-established, inexpensive, and non-invasive method for assessing brain function that is more suitable for routine use than other imaging modalities, such as magnetoencephalography or functional magnetic resonance imaging [7]. Prior studies have used abnormalities in resting-state EEG spectral power to characterize individuals with MDD. Frontal alpha asymmetry [8; 9], which refers to relatively greater left versus right alpha band power, is a consistent EEG marker of MDD. Furthermore, the absolute EEG power at specific channels (e.g., alpha power in C3, P3, O1, O2, F7, and T3 [10]) and the average EEG power over channels in specific regions (e.g., frontal alpha and frontal theta [11]) have also been used to distinguish adults with MDD from healthy controls. In short, EEG power may be used to detect MDD. Because MDD is heterogeneous and yields different symptom profiles across patients, the additional information provided by EEG may be very helpful for clinicians seeking to make an accurate diagnosis. Several studies have, therefore, combined resting-state EEG power features with machine learning to classify individuals as healthy versus depressed. In a study by [PERSON] et al. [10], classification based on the application of linear discriminant analysis (LDA) to power in the delta, theta, alpha, and beta bands achieved accuracies of 67%, 70%, 73%, and 70%, respectively. Our recent classification study [12] also obtained the best results when applied to alpha power. However, we were only able to achieve a classification accuracy of 68.98% using a \(k\)-nearest neighbor (\(k\)-NN) classifier. Thus, classification via resting-state EEG spectral power features is characterized by relatively low accuracy. The LDA classification performance results reported in [10; 12] could represent the upper limit of classification based on spectral power features, but it is likely that applying a more sophisticated classifier such as a support vector machine (SVM) may improve accuracy. Furthermore, employing an MDD-sensitive task to elicit EEG responses that differ strongly between depressed and healthy individuals may also prove helpful [10; 12]. The present study took this approach by applying a novel SVM classifier to EEG data recorded during an emotion induction paradigm completed by adults with MDD and healthy controls. The task used in the current study focused on the induction of positive emotions. We did not use emotionally negative stimuli, because although excessive negative emotion is a prominent characteristic of MDD, it is also commonly found in anxiety. By contrast, anhedonia--reduced ability to derive pleasure from enjoyable experiences--is relatively specific to depression [13]. Therefore, a blunted neural response to positive stimuli may be more diagnostic of depression versus anxiety. Furthermore, anhedonia has been linked to disruption in brain reward circuitry [14; 15; 16], and such abnormalities may have consequences for downstream functions, such as episodic memory [17; 18; 19]. Therefore, abnormal responses to positive stimuli appear to be a reliable and important aspect of depressive illness. To our knowledge, however, no prior study has used positive emotion-induction to improve EEG-based detection of MDD.