An exemplary system includes a set of electrooculogram (EOG) sensors, each including an array of flexible electrodes fabricated on a flexible-circuit substrate, the flexible-circuit substrate operatively connected to an analog-to-digital converter circuitry operatively connected to a wireless interface circuitry; and a brain-machine interface operatively connected to the set of EOG sensors, the brain-machine interface including: a processor; and a memory operatively connected to the processor, the memory having instructions stored thereon, wherein execution of the instructions by the processor causes the processor to: receive EOG signals acquired from the EOG sensors; continuously classify brain signals as control signals via a trained neural network from the acquired EOG signals; and output the control signals.

The present application discloses methods and systems for mobility device control by capturing electroencephalogram (EEG) brain wave data to extract steady-state visually evoked potential (SSVEP) signal data and decoding the SSVEP signal data into at least one command for controlling the mobility device. The SSVEP signals are triggered through visual stimuli on a screen of a device, such as a user device, and are decoded through a machine learning pipeline. At least one of cloud, edge or fog principles may be utilized to enhance response time. The use of the machine learning pipeline and at least one of cloud, edge or fog technology provides an improved mobility device control system response time when compared to the current and prior alternatives for mobility device control systems.

The present invention provides an epidermal multimodal human-drone interfacing system which enable drone operation in dynamic and intricate environments. The interfacing system comprises: a base station configured to: receive hand orientation data of a user and obstacle data within a caution distance from a drone; and generate, on basis of a correlation of the hand orientation data and the obstacle data, respective control commands for providing tactile feedback to a user's fingers, stimulating multiple muscle groups of the user's arm and controlling the drone; a drone control tactile feedback (DCTF) module configured to: collect the hand orientation data of the user and deliver the tactile feedback to the user's fingers; and a neuromuscular electrical stimulation force feedback (NMESF) module configured to: collect obstacle data within the caution distance from the drone and deliver stimulation current to the multiple muscle groups of the user's arm.