The non-intrusive nature of sEMG and its ease of modulation contributes considerably to much of its usage in research. Integration of sEMG into different daily activities ranging from prosthetics [14], robotic control [15, 16, 17, 18], wheelchair control [19, 20], and more is also possible for this reason. In addition, EMG has been used as a serious game control--referred to as exergaming [21, 22, 23]--and the general premise of using EMG-controlled games is that motor skills acquired through them will be translated into useful skills, such as rehabilitation or prosthetic controls. A method in [24] investigated how exergaming improves motor skills and whether there is a transfer of skills over time. The authors reported in-game adaptability and accuracy with minimal or no translation from games to daily activities. A similar method was reported in [23], where the authors developed a system to facilitate rehabilitation exercises to recover patients. The target of the game was to increase motivation and performance and to quantify measurable progress. Researchers have found that exergaming can improve exercise adherence, engage participants, sustain motivation, and result in other benefits [25, 26, 27, 28]. In this paper, we propose to utilize the neck EMG of the left and right sternocleidomastoid (SCM) muscles and the facial EMG of the Masseter muscle to develop a Human-Machine Interface (HMI) for game control. Scientists in different fields have been using SCM EMG to estimate its various responses and applications [29, 30, 31]. The masseter has been touted as one of the strongest muscles in the human body, given its anatomical purpose in jaw movement [32], and, coupled with the fact that upper body muscles are not affected even in spinal cord injury (SCI), the combination forms a good source of HMI for disabled patients [33, 34]. EMG of the neck and face have been used in previous studies as control inputs. In [34], [PERSON] et al. applied EMG of the neck muscle and head turns in an attempt to restore cursor control to patients with tetraplegia. The target of the study was computer-controlled recovery for patients with SCI. They used head orientation and EMG of the facial and neck muscles--users turned their heads or altered muscle contractions to change the position of the computer cursor. The authors further expanded the research into robotic control. In this case, the EMG signal from the neck was used as the input source for the 3D robot command [18]. Other research has also attempted to confirm the usability of neck EMG in feedback and control recovery for disabled individuals. Game controller interfaces for people with disabilities in the literature focusing on motor impairments can be categorized into remapping/modification control inputs and developing alternative controls. In remapping, the target is to remodel or upgrade the standard joystick using software of additional hardware to suit user requirements [35]. Alternative controllers focus on new input signals for analysis. These include, but are not limited to, voice commands, mouth, vision-based gestures [36], and bio-signals [37; 38; 39; 40; 41]. EMG as a control interface for games can be categorized in various degrees of freedom (DoF) or control dimensions [42]. Single click (e.g., in firing command) [21; 23; 43], 1 DoF (e.g., in left and right movement) [26; 27], 2 DoF (e.g., in left, right, top and down movement) [14], or 3 or more DoF (input combination). This paper is a continuation of a previously published article [44], in which we explored the implementation of control interfaces in 1D. This work reports on further developments being made, notably on machine learning (ML) generalizations from trained models and multiple-dimensional controls or 2D controls, by incorporating additional EMG sensors. Target use cases are individuals with Amelia or amputees as control schemes or entertainment interfaces. With a focus on biofeedback, users can control custom-made games to the desired effect.