# Usability and Acceptance Analysis of Wearable BCI Devices
## 1 Introduction  
Neuroergonomics, an interdisciplinary field of study and research into the brain and human behavior, combines neuroscience with ergonomics (or human factors) to maximize the benefits of both [1]. Therefore, the goal is not only to investigate the anatomy and physiology of the brain but also to shed light on how behavior and brain function relate to one another in the workplace and in daily life [2]. Continuous innovation in hardware and software technologies such as sensors, displays, processors, data storage, and algorithms are essential elements in the paradigm shift of computing devices. Wireless connectivity has enabled the creation of a new generation of smart and connected objects with assistive capabilities, ranging from wearable computing devices to connected physical items in the environment like sensors and specialist displays [3, 4]. Wearable devices are defined by [PERSON] et al. [5] as smart electronic devices that come in different types and are placed on or close to the human body to record and analyze psychophysiological data, including blood pressure, heart rate, emotions, and movements, using applications that are installed on the devices or on external devices (such as smartphones that are connected to the cloud). The development of such deviceshas contributed significantly to the improvement of individual and societal quality of life; among the different types of devices, self-quantifying devices stand out as the most obvious example of technological progress [5]. The study of mental workload provides two primary functions in the field of human factors: (i) quantifying the interaction between operators and a set of task demands, technological systems, or operational protocols and (ii) estimating the possibility of performance compromise during operational scenarios, which may be necessary for safety. To date, research on the measurement of mental workload has outgrown the development of theoretical models, and this can be analyzed and evaluated based on performance or through subjective self-assessment and combined with studies in psychophysiology or neurophysiology [6]. The development of advanced portable neuroimaging (or brain imaging) techniques that enable non-invasive examination of the brain at work [1] has led to a revolution in the field of understanding the neural mechanisms that are essential to human attention and performance over the past three decades [7]. Also, the market for neurofeedback systems is expected to expand at a Compound Annual Growth Rate (CAGR) of 7.72% from USD 1.40 billion in 2025 to USD 2.03 billion by 2030. The study of the neural basis of perceptual and cognitive processes, including vision, hearing, memory, decision-making, and planning, in relation to real-world technologies and environments is the focus of neuroergonomics [1]. The structure, function, or pharmacology of the nervous system can be directly or indirectly mapped [8] using brain imaging technologies. [PERSON], in \"The Design of Everyday Things\", emphasizes how all artificial things are designed, focusing on the interaction between technology and people to ensure that products meet human needs while being understandable and usable. Specifically, physiological sensing devices have been used to assess and control psychophysiological states [9; 10; 11], monitor personal health, evaluate stress [12], prevent brain injury [13], and control robots, robotic arms, and devices like wheelchairs by detecting eye movements and brain waves [14; 15; 16]. Additionally, they are employed in cognitive training, stress reduction, and emotion detection [7; 17]. There are two types of neuroimaging techniques: those that detect neural activity directly and those that represent metabolic brain processes linked to neural activity [7]. The most popular in vivo neuroimaging technology is electroencephalography (EEG), which measures electrical activity in the brain from the scalp.