Affective Video Events Summarization using EMD Decomposed EEG Signals (EDES)

Video Summarization is a computer-based technique to generate a shorter version of the original long video for memory management and information retrieval. The existing method for video summarization applies affective (the state of excitement, interestingness, and panic) level of a viewer captured by Electroencephalography (EEG) while watching a video. The traditional methods for extracting features from EEG signals employ Discrete Fourier Transform (DFT). However, DFT is mainly suitable for stationary signals. As EEG signals are non-linear and non-stationary in nature, DFT is not appropriate for EEG signals. In addition to this, the high-frequency components of EEG signals usually contain the distinct properties than the other frequency components. Empirical Mode Decomposition (EMD) technique extracts the different frequency components from high to low from non-linear and non-stationary signals, such EEG. Therefore, we propose a new video summarization method applying EMD Decomposed EEG Signals (EDES) to extract the high-frequency components. The proposed approach calculates Power Spectral Density (PSD) from the high-frequency components and generates a neuronal attention curve for a video. Finally, a video summary is produced by selecting the affective video events from the neuronal attention curve. The experimental results reveal that the proposed approach performs better than the existing state-of-the-art method.