Description:FIELD OF THE INVENTION
This invention relates to Development of a Brain-Controlled Wheelchair Using Emotive EEG and Arduino.
BACKGROUND OF THE INVENTION
Patients with severe PD have substantial immobility problems, which adversely affects their quality of life and autonomy degree. Self-propelled manual wheelchairs have certain physical demands that often wheel chair users are unable to fulfill hence making it difficult for them to maneuver through their environments. This project aims at establishing a brain-controlled wheelchair that requires BCI, a technology that uses the 5-channel Emotiv EEG headset to read the independent mind’s signals and translate them into movement instructions. Using an Arduino microcontroller the system can allow a user to control a wheelchair using only his mind and at the same time includes safety features such as ultrasonic sensors to help the wheelchair detect obstacles in real time. To that end, this novel approach is intended to increase user control and offer a trustworthy transport system to ameliorate the lives of those with mobility limitations.
Emotiv and OpenBCI-based systems: The researchers and developers have designed brain-computer interfaces using Emotiv EEG headsets and OpenBCI thick platforms, which translate the brain signals into pushing the wheel chair and the people have used Bluetooth modules for wireless connection between the headset and the control unit of the wheel chair may be Arduino or Raspberry Pi.
BrainGate: The research program of BrainGate aims at mobility help through brain-controlled interfaces and it has produced systems that let paralyzed patients control wheelchairs and other appliances using signals directly from the brain.
NeuroSky-based projects: NeuroSky, another economical EEG headset, was used in some literatures to design brain-computer interface for wheelchairs whereby signals obtained through EEG are analyzed with machine learning algorithms to infer express thoughts and convert them into directions for movement.
US20170095383A1Intelligent wheel chair control method based on brain computer interface and automatic driving technology.
Research Gap: Proposed intelligent wheel chair control method using brain computer interface and an automatic driving concept integrates BCI with autonomous driving system where the users are able to choose the destination through BCI while the wheelchair itself performs the driving towards the destination through the path planning and PID controller.The difference is that in Our project the wheelchair is controlled directly from brainwave patterns i.e., the movement (forward, backward, sideways) but it lacks the function of navigating on its own, and thus does not contain aspects such as decision-making to determine the path to take.
CN103705352A Intelligent wheelchair based on brain-computer interface and control system and control method thereof
Research Gap:The major difference is that Intelligent wheelchair based on brain-computer interface and control system and control method thereof centers around an intelligent wheelchair with the more flexible and localized control system that is composed of BCI and walking with local path planning although our project mainly uses a 5-channel Emotiv EEG headset to control the wheelchair only by the feature of EEG, and emphasizing on the direct mental state control without path finding capability.
KR20110072730A: Adaptive brain-computer interface device.
Research Gap: The main difference is in analysing an adaptive BCI system as presented in the Adaptive brain-computer interface device which identifies ways of efficiently ordering stimuli that employ P300 signals for use in command selection. On the other hand, our project is based on a method to control a wheelchair through brainwave pattern using Emotiv EEG headset for movement of the wheelchair through movement detection based on real time mobility and sensing of obstacles.
• Limited Accuracy and Reliability: A lot of current BCI systems like the ones that use consumer-electronics such as the Emotiv EEG headset, have unreliable signals, or in inability to give highly accurate mappings between a subject’s thoughts and the movements of a device, which is a problem in control applications, especially those related to mobility assistances.
• User Adaptation and Training: The majority of BCI studies need both the calibration and schooling of the user to decode a specific user’s brain signals, which restrains widely employability and severely limits functional use by those with severe disabilities or other cognitive issues who may not be able to complete these training procedures.
• Cost and Accessibility: Slightly more developed BCI instruments might cost tens of thousands of dollars, which hampers its spread among users. Furthermore, these technologies can be applied into more utilitarian uses, concerning individuals who need wheelchairs or some other comparable equipment, however, again, the incorporation of these technologies is frequently hindered by costs and therefore cannot be easily marketed to users.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The break through project which is the brain-controlled wheelchair focuses on the mobility problem of individuals with severe physical disabilities by using the advance technology of brain-computer interface (BCI). It uses a 5-channel Emotiv EEG headset to pick up brainwaves and in the second stage, an Arduino microcontroller translates these brainwaves into movement instructions for the wheelchair. From the study, users can determine how the wheelchair moves; onward, backward, left, or right just by thinking of a particular direction. Also, ultrasonic tends to give real-time improvement on obstacles detection hence reducing on accidents that may occur due to collision. It is as much about the concept of user independence and autonomy as it is a groundbreaking development in mobility- impaired assistive technology.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The break through project which is the brain-controlled wheelchair focuses on the mobility problem of individuals with severe physical disabilities by using the advance technology of brain-computer interface (BCI). It uses a 5-channel Emotiv EEG headset to pick up brainwaves and in the second stage, an Arduino microcontroller translates these brainwaves into movement instructions for the wheelchair. From the study, users can determine how the wheelchair moves; onward, backward, left, or right just by thinking of a particular direction. Also, ultrasonic tends to give real-time improvement on obstacles detection hence reducing on accidents that may occur due to collision. It is as much about the concept of user independence and autonomy as it is a groundbreaking development in mobility- impaired assistive technology.
BASIC IDEA GENERATED
The proposed technical paper “Development of a Brain-Controlled Wheelchair Using Emotiv EEG and Arduino” aims at developing a wheelchair that is operated by EEG signals. The Emotiv EEG apparatus records the signal from the brain, this signal is then translated into movements for the wheelchair. These commands are relayed to an Arduino microcontroller; the device responsible for the physical control of the wheelchair such as an ability to turn or move forward. This system is designed to offer increased transportation opportunity for the physically disabled through a safe neurosurgical method.
WHOLE PROCESS IN A NUTSHELL:
A brain-controlled wheelchair with the help of Emotiv EEG and Arduino circuit.
A brain controlling a wheelchair is a novel technology for people with physical disabilities who cannot move their limbs. This system utilizes electro enzyme topo graphical signals of the brain to maneuver a wheelchair thus enhancing mobility and enhancing independence among the patients. The machinery that comprises e-Text system are the EEG (Emotiv EEG headset), signal conditioning circuit (computer), micro controller (Arduino), motor driver circuit and the motors (DC).
1. Emotiv EEG Headset records electrical activity of the brain then sends this information wirelessly to a computer. Signal Processing Unit analyses Alpha, Beta, Theta waves and with the help of machine learning capability it gives back movement commands. The DC motors used in the wheelchair are controlled through commands received at the Arduino Microcontroller, which instructs the motor driver to take action processing.
2. Signal Processing Unit interprets brainwave patterns (such as Alpha, Beta, and Theta waves) using machine learning algorithms, which are then translated into movement commands.
3. Arduino Microcontroller receives these commands and signals the motor driver to control the wheelchair’s DC motors.
4. Motor Driver Circuit enhances the signals for the movement of motors thus having forward, backward, as well as turning movements.
The system operates through the following steps:
The EEG headset captures the head maps which represent brain activity and the information is processed in a computer in real time.
The processed data is then transferred to the Arduino, which interfaces the motors of the wheelchair through recognizing user’s thought patterns.
–Because the signals are depicted in real time, the system alters the degree to which the wheelchair responds to the user’s brainwaves in real time.
A development of a Brain-Controlled Wheelchair Using Emotiv EEG and Arduino comprising a wheelchair, a sensor, an EEG acquisition device, a microcontroller, a wheelchair control device, wherein the EEG acquisition device records electrical activity of the brain by the sensors then sends this information wirelessly to a computer, which represent brain activity and the information is processed in a computer in real time Wherein the ultrasonic sensor acts as real-time detector to help a robot avoids an impact with an obstacle.
In another emboodiment the microcontroller allows the user to control a wheelchair using only his mind and at the same time includes safety feature;
Wherein Signal Processing Unit analyses Alpha, Beta, Theta waves and with the help of machine learning capability it gives back movement commands;
Wherein the DC motors used in the wheelchair are controlled through commands received ats the Microcontroller, which instructs the motor driver to take action processing.
Advantages: are given by the concepts of mobile navigation and improving the level of user’s autonomy as the subject has severe motor disorders. That system is flexible and can be extended to accommodate the growing needs, problems include signal accuracy, the training of users, and a slight latency.
In this brain-controlled wheelchair project, one utilizes the 5 channel Emotiv EEG headset to pick focus and relaxation brain signals and convert them into movement commands. These brainwave signals are received by the Arduino microcontroller and the same analyses the signals to formulate control signals to the motors of the wheelchair. Motor drivers regulate the rate and direction of the motors allowing for forward, backward, left and right movements. These ultrasonic sensors act as real-time detectors to help a robot avoid an impact with an obstacle. Further, all mechanical operations have manual controls for backup purposes as well as for general switching.
The new feature in this system is the integration of signals from the brain to control a wheelchair, providing a way of motorized mobility for quadriplegics or other persons with limited limb movement.
, C , Claims:1. A brain-controlled wheelchair system comprising:
• an EEG acquisition device configured to record electrical activity of the brain of a user;
• a signal processing unit configured to process the brain activity signals and convert them into movement commands;
• a microcontroller configured to receive the movement commands and control a wheelchair;
• a motor driver circuit connected to the microcontroller, the motor driver circuit being responsible for controlling one or more motors to move the wheelchair in accordance with the movement commands; and
• an ultrasonic sensor configured to detect obstacles in the path of the wheelchair and provide real-time feedback to prevent collisions.
2. The brain-controlled wheelchair system as claimed in claim 1, wherein the EEG acquisition device is an Emotiv EEG headset, and the headset is configured to wirelessly transmit the recorded brain signals to a computer or processing unit.
3. The brain-controlled wheelchair system as claimed in claim 1, wherein the signal processing unit analyzes brainwave patterns selected from the group consisting of Alpha, Beta, and Theta waves, and uses machine learning algorithms to generate control signals based on the analysis.
4. The brain-controlled wheelchair system as claimed in claim 1, wherein the microcontroller is an Arduino microcontroller, and the microcontroller is responsible for receiving processed brain signals and sending movement instructions to the motor driver circuit.
5. The brain-controlled wheelchair system as claimed in claim 1, wherein the ultrasonic sensor continuously scans the environment in front of the wheelchair to detect objects or obstacles and sends a feedback signal to the microcontroller to adjust the wheelchair's movement to avoid collision.
6.The brain-controlled wheelchair system as claimed in claim 1, wherein the motor driver circuit is configured to control the wheelchair's motors to perform movements selected from the group consisting of forward, backward, left, and right.
7. The brain-controlled wheelchair system as claimed in claim 1, further comprising a backup manual control system that allows a user to manually control the wheelchair in case of failure or malfunction of the brain-controlled system.
8. A method for controlling a wheelchair using brain-computer interface technology, comprising:
• capturing brain activity signals from a user using an EEG acquisition device;
• processing the brain activity signals to identify specific brainwave patterns;
• translating the identified brainwave patterns into movement commands using a signal processing unit;
• transmitting the movement commands to a microcontroller;
• controlling a motor driver circuit connected to the microcontroller to move the wheelchair in accordance with the movement commands;
• using an ultrasonic sensor to detect obstacles in the wheelchair's path and adjusting the wheelchair's movement to avoid collision based on the detected obstacles.
9. The method as claimed in claim 8, wherein the brainwave patterns identified include Alpha, Beta, and Theta waves, and the movement commands are determined based on the analysis of these brainwave patterns.
10. The method as claimed in claim 8, wherein the microcontroller is an Arduino microcontroller and the signal processing unit is configured to utilize machine learning algorithms to refine the movement commands based on the detected brainwave patterns.