## 1 Introduction  
This research utilizes electromyography (EMG) technology as an alternative wheelchair control option. Other options that are available for patients with mobility and dexterity challenges include brain-computer interfaces (BCIs), head-tracking interfaces, and eye-tracking interfaces [1, 2, 3, 4, 5]. Specific BCI methods often convert electroencephalography (EEG)signals from the brain into commands that can be used for wheelchair movements [1]. A BCI equips patients with an intuitive system that accurately projects user intentionality in controlled settings [2]. However, EEG signals are sensitive to a variety of factors and need to be effectively amplified due to background noise when used in real-world environments [2]. The tracking of head movement is another alternative interface for hands-free wheelchairs [3]. Although this system has been shown to provide freedom of control, additional considerations should be taken into account for specific populations. In the case of neurodegenerative disorders, the eventual loss of muscle control can cause difficulty in changing head positions. The virtual buttons displaying wheelchair directions (forward, backward, left, right) can then be pressed with a glance through an eye-tracking interface [4]. The eye-tracking abilities of this interface require minimal head movement and can be applied to a wide range of populations, although users with complex conditions or those with low vision may face difficulties in operating systems. EMG interfaces measure electrical signals transmitted by muscular contraction [5; 7; 8]. Leveraging hardware and software signal processing, each intentional muscle active contraction burst can be isolated. The relative magnitude of these bursts, calibrated to the patient, can be assessed and then used to trigger movement of a wheelchair based on set threshold values. The authors have been advancing research on this EMG-wheelchair interface, which has been shown to be useful for improving mobility among ALS patients [6]. Challenges with EMG-based technology include difficulty with muscle isolation and the need for training methods [6]. The advantages of EMG systems include the minimally invasive nature of implementation and the flexibility of placement, which may be required among populations with muscle deterioration. Early advancements in BCI, EMG, and eye-tracking interfaces laid the groundwork for later developments. More recently, hybrid brain-computer interfaces that combined BCI systems with steady-state visual evoked potentials (SSVEP) showed promise in practical applications. The multi-modality of this system increases accuracy and decreases fatigue compared to an EEG-based system only [9]. Another study sought to evaluate the usability of a BCI system coupled with eye tracking, determining that this hybrid system allows compensation when a single-modality decreases in performance [10]. Augmented reality offers another channel of user input, which further shows how multi-modal system integration is increasingly being implemented to improve the user experience [11]. The need for these types of alternative devices for mobility and dexterity is heightened for patient groups with complex and progressive pathologies, including patients with neurodegenerative disorders. Neurodegenerative disorders are progressive diseases caused by the degeneration of the central nervous system (CNS) and can result in a decline in independent mobility and hand dexterity [12]. Amyotrophic lateral sclerosis (ALS) is the most common motor neuron disease, with a prevalence rate of 3.40/100,000 in North America and 5.40/100,000 in Europe [13]. The onset and progression of the disease lead to weakness in the limbs and trunk, which can negatively impact a person's ability to perform activities of daily living [14]. Oftentimes, movement of the upper and lower extremities is severely compromised and can limit the use of a traditional joystick for powered wheelchairs.