# Applying Neural Networks with Time-Frequency Features for the Detection of Mental Fatigue
## 1 Introduction  
Sleep deprivation comprises a state provoked by either a complete lack of sleep or a suboptimal duration of rest. Evidence-based studies have demonstrated a significant impact of sleep deprivation on information processing and overall cognitive performance [1], with its effects varying based on the cognitive requirements and the brain areas involved. Under this state, mental fatigue emerges as a key outcome, resulting in concentration difficulties and low vigilance. Consequences are particularly evident in the context of working memory, which by definition entails rapid information manipulation and task execution [2]. As such, mental fatigue is associated with compromised performance from a working memory perspective, which often proves crucial in specialized settings, such as healthcare institutions. Specifically, hospital staff typically undergo prolonged periods of demanding cognitive activity during their shifts, presenting increased probability of burnout, which in turn poses a potential risk on patient safety [3]. In such circumstances, prompt identification of mental fatigue levels that may induce declined performance and related errors in hospital settings is essential. In order to assess mental fatigue states, several electrophysiological recording modalities have been utilized, providing indications of the complex brain interactions and therefore insights of the cognitive mechanisms governing cognitive exhaustion [4; 5]. In this view, electroencephalography (EEG) has been showcased as an invaluable apparatus due to its ability to detect brain activity (non-invasively) in the form of neuronal oscillations, enabling researchers to quantify the impact on cognitive functions under various levelsof mental load [6]. The main classification problem lies on the state of the individual being mentally rested (herein referred to as Reseted class) or mentally fatigued (Fattigued class). Most frequently, signal processing techniques are employed in order to develop mental fatigue detection algorithms. These techniques involve the use of mathematical algorithms to analyze and extract relevant information from EEG signals, without the use of data-driven development. For instance, several studies include wavelet transformation and independent component analysis to analyze EEG data and identify changes in brain activity that are indicative of mental fatigue [7,8]. However, these approaches require manual intervention and refactoring that might deter a fully automated detection of mental fatigue. Another widely used approach is machine learning, which involves the use of statistical models to analyze and interpret (EEG) data in order to identify patterns and features (e.g., power spectrum) that are indicative of mental fatigue [9]. However, such techniques require manual feature extraction in order to perform optimally, while the extracted features are usually subject dependent [9] and thus not ideal for real-world and real-time paradigms. On this premise, artificial intelligence (AI) has become a powerful tool, with deep learning (DL) designs delivering automatic feature detection and extraction, unleashing outstanding advances in classification, segmentation, and prediction tasks. As such, many in fatigue detection studies have utilized DL algorithms due to their high classification performance (compared to conventional machine learning methods), especially when utilizing solely EEG signals (disregarding other modalities, such as electrooculograms or facial video data) [10,11]. Furthermore, in pursuit of metrics with high discriminative power, even more complex architectures have been proposed in literature, with convolutional layers and recursive neural networks achieving promising results, however at the cost of increasing complexity [12]. In this regard, most of these implementations use multi-layered deep convolutional neural networks (CNNs) that require high-end hardware and a substantial amount of time for both training and prediction phases.