Search

Situational awareness detection and characterization of mental states have a vital role in medicine and many other fields. An electroencephalogram (EEG) is one of the most effective tools for identifying and analyzing cognitive stress. Yet, the measurement, interpretation, and classification of EEG sensors is a challenging task. This study introduces a novel machine learning-based approach to assist in evaluating situational awareness detection using EEG signals and spiking neural networks (SNNs) based on a unique spike continuous-time neuron (SCTN). The implemented biologically inspired SNN architecture is used for effective EEG feature extraction by applying time–frequency analysis techniques and allows adept detection and analysis of the various frequency components embedded in the different EEG sub-bands. The EEG signal undergoes encoding into spikes and is then fed into an SNN model which is well suited to the serial sequence order of the EEG data. We utilize the SCTN-based resonator for EEG feature extraction in the frequency domain which demonstrates high correlation with the classical FFT features. A new SCTN-based 2D neural network is introduced for efficient EEG feature mapping, aiming to achieve a spatial representation of each EEG sub-band. To validate and evaluate the performance of the proposed approach, a common, publicly available EEG dataset is used. The experimental results show that by using the extracted EEG frequencies features and the SCTN-based SNN classifier, the mental state can be accurately classified with an average accuracy of 96.8% for the common EEG dataset. Our proposed method outperforms existing machine learning-based methods and demonstrates the advantages of using SNNs for situational awareness detection and mental state classifications. [ABSTRACT FROM AUTHOR]

Many studies discuss the impact of music programs on prison inmates. However, few studies have investigated the impact of music programs on formerly incarcerated individuals (FIIs), and none of them have focused on the programs' characteristics. This phenomenological study explores the characteristics of a music-based rehabilitation program for FIIs. Five FIIs and three staff members who participated in a group music program were interviewed. The following four themes emerged: Continuity —the importance of the FIIs' previous positive acquaintance with the program while still being in prison. Egalitarian approach —the importance of the staff treating the FIIs with understanding, tolerance, non-judgmentalism, and the use of an eye-level approach. Cultural sensitivity —the staff members' sensitivity to the FIIs' different backgrounds to form a connection. Playfulness —the program's ability to break free from a "fight-or-flight" mode and experience a counter mode of playfulness. The findings are discussed through the perspective of positive criminology. [ABSTRACT FROM AUTHOR]