# Situational Awareness Classification Based on EEG Signals and Spiking Neural Network
## 1 Introduction  
The advancement of neuromorphic computing has amplified the interest in utilizing biologically plausible networks for classifying physiological signals [1; 2; 3; 4]. SNNs are characterized by low energy consumption and a low computational cost, and therefore they are most suitable for applying machine learning for edge application [2]. These include indicators of emotional arousal states such as respiratory activity, cardiac activity, EEG signals, and electrodermal activity response [5; 6]. Such neural signals can be obtained using a variety of techniques, including electroencephalography (EEG) and functional magnetic resonance imaging, providing insights into cognitive and mental states [7; 8]. Situational awareness (SA) pertains to an individual's understanding of their complex operating environment. It involves comprehending what has happened, what is currently happening, and what might happen based on the surrounding context. Evaluation of SA is essential in tasks that require processing multiple streams of information, as it helps human operators effectively navigate and respond well to their environment [9; 10]. EEG brain activity recordings are highly promising for assessing SA. EEG signals capture signals from all sensory inputs, providing deep insights into an individual's physiological state. It is widely used to monitor and predict mental states such as workload, mental fatigue, sleepiness, and drowsiness. Given the close relationship between these mental states and SA, many studies utilize EEG to evaluate situational awareness [11; 12]. EEG analysis involves a variety of methods for processing and interpreting the electrical activity of the brain. These methods can be categorized into time domain [13], frequency domain [14; 15], and time-frequency domain analysis [16; 17], along with advanced techniques involving deep neural networks (DNNs), machine learning, and statistical modeling [18; 19]. Time domain analysis includes event-related potentials (ERPs) and EEG amplitude and latency measurements [20]. Frequency domain techniques include power spectral density estimation methods based on time-varying parameter modeling [21; 22], while time-frequency analysis mainly includes short-time Fourier transforms (STFTs) and wavelet transforms [23]. Filtering techniques and preprocessing are used to remove noise and artifacts from the EEG signal. [PERSON] et al. proposed a classification network for EEG signals based on cross-domain features related to brain activities using a multi-domain attention mechanism [7]. [PERSON] et al. utilized the support vector machine method for monitoring mental attention states (_focused_, _unfocused_, and _drowsy_) by using EEG brain activity imaging [24]. [PERSON] at el. evaluated various spike encoding techniques for time-dependent signal classification through a spiking convolutional neural network including signal preprocessing, consisting of a bank of filters [25; 26]. [PERSON] [27] proposed the use of the well-known \(\Delta\Sigma\) encoding scheme in the design of an SNN's input layer to convert analog sensor voltages into spike trains with firing rates that are linearly proportional to the input voltage. Pulse-density modulation (PDM), also known as delta-sigma modulation (DSM), is a form of modulation used to represent an analog signal with a binary signal. The analog signal's amplitude is converted into binary sequences corresponding to the amplitude [28]. A PDM bitstream is encoded from an analog signal through the process of one-bit delta-sigma modulation. This process uses a one-bit quantizer which produces either a one or zero, depending on the analog signal's amplitude. A high density of ones occurs at the peaks of the sine wave, while a low density of ones occurs at the troughs of the sine wave. [PERSON] et al. presented a novel approach utilizing SNN and EEG processing techniques for emotion state recognition. The method employs a discrete wavelet transform (DWT) and fast Fourier transform (FFT) for extracting EEG signals [29].