## 1 Introduction  
Individuals with tetraplegia from, e.g., amyotrophic lateral sclerosis (ALS) experience progressive muscle weakness and loss of motor function. As a result, individuals with ALS may suffer extensive physical [1, 2] and emotional distress [3, 4] and must rely on caregivers for simple daily tasks. Thus, assistive technologies (ATs) seek to enhance the quality of life for individuals with severe physical disabilities by improving or substituting motor functions, consequently boosting their autonomy [5, 6, 7]. However, as ALS progresses to the late stages, most commercially available interfaces, such as joystick controllers [8, 9], tongue-based systems [10, 11], and eye-gaze-operated devices [12, 13], become unusable. Brain-Computer Interfaces (BCIs) bypass the lack of motor function, as they translate brain activity directly into control commands for the assistive device [14]. Electroencephalography (EEG) is a non-invasive method for recording brain activity, commonly used in BCI development given its high time resolution and relatively low cost compared with other techniques. Within the scope of AT, BCI systems have been employed in devices for assisted communication, computer cursor control and web browsing, mobility, and environmental control [15, 16, 17, 18, 19]. State-of-the-art BCI-based directional control systems, such as those used to obtain control of robotic devices, often require the user to split their attention between the task and the BCI feedback, on, e.g., a computer screen. This is critical in instances of environmental control, such as operating a robotic arm to pick up an object, as distractions from both the task and surrounding environment should be minimal for safety reasons. Dual tasking also increases the user's mental workload [20]. Systems that rely on visual stimulation (steady-state visually evoked potentials, SSVEP, and P300) often exhibit this problem since they make the user avert their eyes from the task and require extended periods of sustained high mental workload. Visual displays can encumber the user by blocking their field of view, and continuous strong visual stimuli may lead to fatigue and discomfort [21, 22, 23], reducing signal quality and impacting system performance [24]. While Motor Imagery (MI)-based systems do not share these bottlenecks, they still require extensive training and suffer from target amount limitations since their accuracy decreases when the number of classes increases [25, 26]. For that reason, many MI-based systems use a few classes [27, 28, 29] or implement MI as a fast brain switch to select commands from a cyclic menu [30, 31]. Albeit to a lesser extent, parieto-occipital alpha waves (or the alpha rhythm) have been considered for asynchronous BCI system applications [32], given their high signal-to-noise ratio and unresponsiveness to environmental conditions. The alpha rhythm is a rhythmic pattern in EEG observed at frequencies between 8 and 12 Hz and is typically found over the occipital cortex [17]. Previous findings have demonstrated that the alpha rhythm can be suppressed by visual attention; conversely, periods of mental inactivity with closed eyes trigger an increase in alpha power [33]. Alpha band activity has been studied both in hybrid systems [34, 35] and on its own for simple switch controls and directional controls, such as in [PERSON] et al. [36]. Numerous studies have proposed EEG-based or hybrid BCI for controlling assistive robotic manipulators (ARMs). Many implement manual end-point control strategies, in which the user is in direct control of the movement of the robot's end-effector [23, 28, 37, 38, 39, 40]. These control strategies are flexible and intuitive but are often unable to provide fast and precise continuous control without fatiguing the user. For that reason, other approaches using goal selection or shared control strategies have been proposed. For example, [PERSON] et al.