Classification of test-anxious individuals using Event-Related Potentials (ERPs)

Individuals with test anxiety always treat tests/examinations as a potential threat. This cognitive mode impairs these individuals’ cognition, attention and emotions. A traditional method classifying subjects either as high or low on test anxiety (i.e., HTA or LTA, respectively) relies on questionnaire data. Questionnaire data may be unstable due to the subjective nature of participants’ attitudes, implying a reduced classification accuracy. In search for higher levels of (data) stability and classification accuracy a new classification approach is proposed. This new approach overcomes subjective data’s negative impact on classification accuracy by relying on event-related potential (EPR) data (also referred to as ERPs), objective (multivariate, longitudinal) data which adequately capture participants’ reactions to relevant stimuli (over time). However, as ERP data may still be somewhat unstable due to individual differences between participants, (machine) learning algorithms are adopted as their ‘learning’ feature may increase both the stability of ERP data and classification accuracy. This study recruited 57 HTA participants and 25 LTA participants based on: (a) Test Anxiety Scale (TAS) scores, and (b) (two) specialists’ psychological diagnostic results on a single participant. Reliance on the emotional Stroop (ES) paradigm in combination with ERP technology enabled the assessment of participants’ cognitive mode related to test anxiety. In ES, the information on the ERP components P1, P2, N2, P3 and LPP ERP were selected as input for seven commonly used machine learning algorithms: Convolutional Neural Network (CNN), Logistic Regression (LR), K Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Recurrent Neural Network (RNN). To compare the classification accuracy of these algorithms (using the complete sample of HTA and LTA subjects) important indexes (i.e., accuracy and F1-score) were calculated and compared across these algorithms. The results showed that: (a) the ERPs data collected in ES allow effective differentiation between HTA and LTA (P1: F (1, 80) = 11.68, p < 0.001, η 2 = 0.13; P2: F (1, 80) = 14.10, p < 0.001, η 2 = 0.15; N2: F (1, 80) = 28.55, p < 0.001, η 2 = 0.26; P3: F (1, 80) = 22.41, p < 0.001 , η 2 = 0.22; LPP: F (1, 80) = 16.92, p < 0.001 , η 2 = 0.18); (b) classification on the basis of ERP data using machine learning algorithms shows high accuracy and stability, that is the classification accuracy of all seven algorithms is found to be high as evidenced by an accuracy index of 71.8% or higher (CNN: 86.5%, LR: 80.3%, KNN: 71.8%, SVM: 79.0%, RF: 73.1%, ANN: 82.7%, and RNN: 79.2%) and an F1-score of 0.814 or higher (CNN: 0.911, LR: 0.868, KNN: 0.817, SVM: 0.865, RF: 0.814, ANN: 0.882, and RNN: 0.870); (c) CNN outperforms the other six common machine learning algorithms showing both the highest accuracy index and F1-score. Moreover, as over and above this (relative) superiority CNN combines the (technical) property known as ‘shift invariance’ and robustness to noise, the algorithm may be considered ideal for effectively classifying test anxious individuals using ERP data. It is concluded that: (a) as manifested by its ‘discriminatory’ nature and stable classification performance (as evidenced by all machine learning algorithms’ favorable values for all important indices) reliance on the ES paradigm enables machine learning leading up to effective diagnosis of test anxiety; and (b) participants’ classification into HTA and LTA by relying on ERP data which are subsequently analyzed by means of the machine learning algorithm CNN is (most) effective (i.e., as benchmarked against six other commonly used machine learning algorithms). Consequently, using ES in combination with ERP technology and the CNN machine learning algorithm can be conceived as an ideal method for diagnosing test anxiety

Algorithm · Cognitive psychology · Electroencephalography · Event-related potential · Machine learning · Physics · Computer Science · EEG and Brain-Computer Interfaces · Neuroscience · Psychology · Artificial Intelligence

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