NextStep Neuro
NextStep Neuro is an assistive mobility system designed to help people with paralysis control a robotic car using their own brain and muscle signals. The system uses EEG (brainwaves) and EMG (muscle activity) to send wireless commands to a robot, allowing movement without any physical controller. Non-invasive electrodes capture EEG and EMG signals from the user. These signals are processed and sent via Bluetooth Low Energy (BLE) to an ESP32-based robotic car, which then moves forward, turns, or stops using motor drivers and DC motors. By combining brain and muscle signals, NextStep Neuro offers more reliable control than using EEG alone. It serves as a low-cost, real-world prototype for future neuro-controlled wheelchairs and assistive mobility devices.
Created by
shaikshahirsiddiqui
Tier 1
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shaikshahirsiddiqui
added to the journal ago
The Main Mother Board to Read EEG and EMG Readings
I researched compact microcontroller boards capable of handling low-voltage biosignals, multiple analog inputs, and stable serial communication. Based on availability, compatibility, and expandability, I chose the NPG Lite board to directly interface with EEG and EMG modules, process incoming signals, and transmit clean, usable data to the motor control system.
shaikshahirsiddiqui
added to the journal ago
System Architecture & Control Design
I focused on designing the full working architecture of an EEG–EMG controlled mobility system. I analyzed how brain beta waves and muscle signals can be mapped to movement commands and defined the complete signal flow from electrodes to motor actuation. I planned electrode placement, skin preparation, and biopotential acquisition for reliable EEG and EMG detection. I designed the wireless communication using Bluetooth Low Energy, where an ESP32 receives control data and drives the motors through a motor driver. I also finalized the robot connection logic and control modes (forward, turning, reverse, stop), and outlined how this system can be scaled and adapted for a wheelchair with added safety considerations.
shaikshahirsiddiqui
added to the journal ago
Initial Research and System Design for an EEG–EMG Controlled
I worked on the foundational research and system design. This included studying brainwaves (such as alpha and beta waves) to understand how intentional mental states can be used for control, and identifying how EEG and EMG signals can be captured reliably using a custom BCI setup. I also finalized the required components and designed the control architecture, where an ESP32 module wirelessly relays processed signals via Bluetooth Low Energy to the wheelchair system, forming the basis for building and implementing the project.
How Does it Work?
The ESP32 receives EEG–EMG control commands via Bluetooth Low Energy from the BCI system. The ESP32 is connected to a motor driver, which controls two DC motors mounted on the robotic chassis. Based on the received commands, the motor driver regulates motor direction and speed, enabling the robot to move forward, turn, or stop.
shaikshahirsiddiqui
started NextStep Neuro ago
1/19/2026 12 AM - Initial Research and System Design for an EEG–EMG Controlled
I worked on the foundational research and system design. This included studying brainwaves (such as alpha and beta waves) to understand how intentional mental states can be used for control, and identifying how EEG and EMG signals can be captured reliably using a custom BCI setup. I also finalized the required components and designed the control architecture, where an ESP32 module wirelessly relays processed signals via Bluetooth Low Energy to the wheelchair system, forming the basis for building and implementing the project.
How Does it Work?
The ESP32 receives EEG–EMG control commands via Bluetooth Low Energy from the BCI system. The ESP32 is connected to a motor driver, which controls two DC motors mounted on the robotic chassis. Based on the received commands, the motor driver regulates motor direction and speed, enabling the robot to move forward, turn, or stop.
1/19/2026 3 PM - System Architecture & Control Design
I focused on designing the full working architecture of an EEG–EMG controlled mobility system. I analyzed how brain beta waves and muscle signals can be mapped to movement commands and defined the complete signal flow from electrodes to motor actuation. I planned electrode placement, skin preparation, and biopotential acquisition for reliable EEG and EMG detection. I designed the wireless communication using Bluetooth Low Energy, where an ESP32 receives control data and drives the motors through a motor driver. I also finalized the robot connection logic and control modes (forward, turning, reverse, stop), and outlined how this system can be scaled and adapted for a wheelchair with added safety considerations.
1/19/2026 6 PM - The Main Mother Board to Read EEG and EMG Readings
I researched compact microcontroller boards capable of handling low-voltage biosignals, multiple analog inputs, and stable serial communication. Based on availability, compatibility, and expandability, I chose the NPG Lite board to directly interface with EEG and EMG modules, process incoming signals, and transmit clean, usable data to the motor control system.