## 3 State of the Art
### EEG-Based Brain-Computer Interface Approaches in Collaborative Robot Control  
In this sub-section, it is intended to analyze different research and technological advances in the area of brain signal capture through EEG applied to collaborative robotics. The purpose is to address the possibility of improving the understanding and execution of robotic systems to make them more intuitive and adaptive to a human being. One of the most recent studies in the field of collaborative robotics, combining brain activity measurement through EEG and machine learning, is the research published by the University of Pennsylvania in 2022. The paper discusses the use of EEG signals to measure peoples' trust levels in collaborative construction robots. EEG signals provide valuable information about human brain activity and cognitive states, including trust, during human-robot collaboration [33]. EEG signals are also used for determining mental states, electroencephalography sensors are able to measure brainwave frequencies such as delta, theta, alpha, beta, and gamma waves. Gamma waves (25-45 Hz) provide important insights related to intentional activities, multitasking, and creativity. Beta waves (12-38 Hz)  
Figure 3: Percentage increase in publications across topic clusters over time (2012–2023). include low beta (12-15 Hz) for idle states, beta (15-23 Hz) for attention, and high beta (23-38 Hz) for stress and difficult activities. Alpha waves (8-12 Hz) are associated with relaxed concentration. Theta waves (4-8 Hz) appear during REM sleep, deep meditation, or flow states. Lastly, delta waves (0.5-4 Hz) signify dreamless sleep [33; 34] (See Figure 4). The incorporation of brain wave signals, specifically alpha and beta waves, into the observation space of a RL agent is meaningful due to their direct relationship with stress and related emotions. Alpha waves are typically associated with states of relaxation and meditation, while beta waves are linked to increased alertness and anxiety [34]. These signals can be processed and subsequently included in the observation space, enabling the RL agent to interpret them and act accordingly [33]. Additionally, integrating these signals into the agent's reward function can provide valuable insights into behavioral variations, thereby enhancing the agent's ability to refine its strategies and responses in scenarios involving stress and relaxation. However, EEG signals can be contaminated by other frequency signals, both intrinsic and extrinsic, resulting in a reduction in the original trust signal quality. In order to address this, some studies used a fixed-gain filtering method to reduce extrinsic components and utilized independent component analysis (ICA) to remove intrinsic components from EEG signals. Once the filtering was done, the results in the EEG measurements were significantly cleaner [33; 35]. After the reduction was done, 12 trust related characteristics from the EEG signals that span the temporal and frequency domains were extracted. These features were calculated from segmented EEG data, and this information was then used for machine learning model training. In order to evaluate the amount of trust levels in collaborative robots several supervised learning algorithms [36] are used, including k-nearest neighbors (k-NN), support vector machine (SVM) [37], and random forest. Among the supervised learning algorithms used, the k-NN outperforms the others showing the highest accuracy at approximately 88%. Once machine learning algorithms are applied, a test with human participants involving building tasks can be conducted to determine trust levels in different robot collaboration scenarios. The results show that higher levels of trust are achieved while working with semi  
Figure 4: The five different brain waves: Delta, theta, alpha, beta, and gamma. autonomous robots. However, working with an autonomous robots lead to lower levels of trust due to the sense of not having any control on the robot.