Objective. Epilepsy is a common neurological disorder in which patients suffer from sudden and unpredictable seizures. Seizures are caused by excessive and abnormal neuronal activity. Different methods have been employed to investigate electroencephalogram (EEG) data in patients with epilepsy. This paper introduces a simple yet accurate array-based method to study and predict seizures. Approach. We use the CHB-MIT dataset (all 24 cases), which includes scalp EEG recordings. The proposed method is based on the random matrix theory. After applying wavelet decomposition to denoise the data, we analyze the spatial coherence of the epileptic recordings by looking at the width of the covariance matrix eigenvalue distribution at different time and frequency bins. Main results. We train patient-specific support vector machine classifiers to distinguish between interictal and preictal data with high performance and a false prediction rate as low as 0.09 h−1. The proposed technique achieves an average accuracy, specificity, sensitivity, and area under the curve of 99.05%, 93.56%, 99.09%, and 0.99, respectively. Significance. Our proposed method outperforms state-of-the-art works in terms of sensitivity while maintaining a low false prediction rate. Also, in contrast to neural networks, which may achieve high performance, this work provides high sensitivity without compromising interpretability.