Controlling an arduino robot using Brain Computer Interface
01 Oct 2014-pp 1-5
TL;DR: This paper establishes an application to control a robot on the Arduino platform by the use of a BCI system, which does not require training for individual users and achieves around 96% accuracy using computationally inexpensive feature extraction and classification techniques.
Abstract: The ability to acquire Electroencephalogram (EEG) signals from the brain has led to the development of Brain Computer Interfaces (BCI), which capture signals generated by the physical processes in the brain and use them to control external devices. In this paper, we establish an application to control a robot on the Arduino platform by the use of a BCI system, which does not require training for individual users. We present the design and development of a BCI processing pipeline built on open-source platforms using the Emotiv EEG headset. Our system achieves around 96% accuracy using computationally inexpensive feature extraction and classification techniques, namely, band power and Support Vector Machines (SVM). We are also able to guide a robot's movement efficiently using multiple intents.
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06 Apr 2016
TL;DR: The qualitative approach of this project is to develop a system which minimizes the working cost and also reduces the time for digging operation and seed sowing operation by utilizing solar energy to run the agribot.
Abstract: The Discovery of Agriculture is the first big step towards civilized life, advancement of agricultural tools is the basic trend of agricultural improvement. Now the qualitative approach of this project is to develop a system which minimizes the working cost and also reduces the time for digging operation and seed sowing operation by utilizing solar energy to run the agribot. In this machine, solar panel is used to capture solar energy and then it is converted into electrical energy which is used to charge battery, which then gives the necessary power to a shunt wound DC motor. Ultrasonic Sensor and Digital Compass Sensor are used with the help of Wi-Fi interface operated on Android Application to manoeuvre robot in the field. This brings down labour dependency. Seed sowing and digging robot will move on various ground contours and performs digging, sowing the seed and covers the ground by closing it. The paper spells out the complete installation of the agribot including hardware and software facet.
33 citations
Cites background from "Controlling an arduino robot using ..."
...The future scope for this paper is not only detecting obstacle but also avoiding it successfully without disturbing the main course of the system [9]....
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TL;DR: The use of low-cost electroencephalography (EEG) devices has become increasingly available over the last decade as discussed by the authors and one of these devices, Emotiv EPOC, is currently used in a wide variety of settings, including brain-computer interface (BCI) and cognitive neuroscience research.
Abstract: BACKGROUND Commercially-made low-cost electroencephalography (EEG) devices have become increasingly available over the last decade. One of these devices, Emotiv EPOC, is currently used in a wide variety of settings, including brain-computer interface (BCI) and cognitive neuroscience research. PURPOSE The aim of this study was to chart peer-reviewed reports of Emotiv EPOC projects to provide an informed summary on the use of this device for scientific purposes. METHODS We followed a five-stage methodological framework for a scoping review that included a systematic search using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. We searched the following electronic databases: PsychINFO, MEDLINE, Embase, Web of Science, and IEEE Xplore. We charted study data according to application (BCI, clinical, signal processing, experimental research, and validation) and location of use (as indexed by the first author’s address). RESULTS We identified 382 relevant studies. The top five publishing countries were the United States (n = 35), India (n = 25), China (n = 20), Poland (n = 17), and Pakistan (n = 17). The top five publishing cities were Islamabad (n = 11), Singapore (n = 10), Cairo, Sydney, and Bandung (n = 7 each). Most of these studies used Emotiv EPOC for BCI purposes (n = 277), followed by experimental research (n = 51). Thirty-one studies were aimed at validating EPOC as an EEG device and a handful of studies used EPOC for improving EEG signal processing (n = 12) or for clinical purposes (n = 11). CONCLUSIONS In its first 10 years, Emotiv EPOC has been used around the world in diverse applications, from control of robotic limbs and wheelchairs to user authentication in security systems to identification of emotional states. Given the widespread use and breadth of applications, it is clear that researchers are embracing this technology.
15 citations
01 Nov 2016
TL;DR: The results obtained indicate that the proposed approach is effective for detecting the eye-wink commands with a good rate of accuracy (over 93%) and allowed the development of a Head-Computer Interface that enables complete interaction with a robotic arm.
Abstract: A relative simple approach based on the computation of the area of a parametric curve produced by the 2D space representation of a set of parametric experimental functions defined by the signals of only two active EEG electrodes of a low cost neuroheadset (Emotiv EPOC) is proposed on this paper for the fast recognition of eye winks activity as control commands. This approach together with the use of the signals from the gyroscope available in the EPOC device, allowed the development of a Head-Computer Interface that enables complete interaction with a robotic arm. The results obtained indicate that the proposed approach is effective for detecting the eye-wink commands with a good rate of accuracy (over 93%).
14 citations
Cites background from "Controlling an arduino robot using ..."
...…sense, in recent years, there has been a considerable increase in research approaches about controlling robots by using signals of biological nature such as electroencephalographic (EEG) and electromyographic (EMG) signals, for allowing the human brain to interact with control computers [2] [3]....
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01 Jul 2021
TL;DR: The results of testing the value of the ultrasonic sensor distance found different conditions that occur, and the condition of the prototype cleaning robot for the road floor cleaning is obtained, while the distance <15 cm, the condition for the prototype of the street floor cleaning robot has stopped.
Abstract: The entire floor cleaning robot is divided into several parts, namely consisting of an Ultrasonic Sensor, Motor Shield L298, Arduino Uno microcontroller, Servo, and Dc Motor. This tool works when the Arduino Uno microcontroller processes the ultrasonic sensor as a distance detector and a DC motor as a robot driver, then the DC motor is driven by the Motor Shield L298. When an ultrasonic sensor detects a barrier in front of it, the robot will automatically look for a direction that is not a barrier to the floor cleaning robot. The distance value on the sensor has been determined, that is, when the distance read by the ultrasonic sensor is below 15 cm. The results of testing the value of the ultrasonic sensor distance found different conditions that occur. In a distance of> 15 cm, the condition of the prototype cleaning robot for the road floor cleaning is obtained, while the distance <15 cm, the condition for the prototype of the street floor cleaning robot has stopped.
13 citations
Cites methods from "Controlling an arduino robot using ..."
...Then an automatic floor cleaning robot was designed using an ultrasonic sensor was studied by gargava [10]....
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TL;DR: This review gives an overview of studies found in the recent scientific literature, reporting measurements of biosignals such as ECG, EMG, sweat and other health-related parameters by single circuit boards, showing new possibilities offered by Arduino, Raspberry Pi etc. in the mobile long-term acquisition of biosignedals.
Abstract: To measure biosignals constantly, using textile-integrated or even textile-based electrodes and miniaturized electronics, is ideal to provide maximum comfort for patients or athletes during monitoring. While in former times, this was usually solved by integrating specialized electronics into garments, either connected to a handheld computer or including a wireless data transfer option, nowadays increasingly smaller single circuit boards are available, e.g., single-board computers such as Raspberry Pi or microcontrollers such as Arduino, in various shapes and dimensions. This review gives an overview of studies found in the recent scientific literature, reporting measurements of biosignals such as ECG, EMG, sweat and other health-related parameters by single circuit boards, showing new possibilities offered by Arduino, Raspberry Pi etc. in the mobile long-term acquisition of biosignals. The review concentrates on the electronics, not on textile electrodes about which several review papers are available.
10 citations
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TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
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TL;DR: EELAB as mentioned in this paper is a toolbox and graphic user interface for processing collections of single-trial and/or averaged EEG data of any number of channels, including EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), Independent Component Analysis (ICA) and time/frequency decomposition including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling.
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"Controlling an arduino robot using ..." refers methods in this paper
...For processing the EEG data, which includes preprocessing by band-pass filtering and band-power feature extraction, we use EEGLAB [5], a MATLAB based EEG signal processing toolbox....
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TL;DR: With adequate recognition and effective engagement of all issues, BCI systems could eventually provide an important new communication and control option for those with motor disabilities and might also give those without disabilities a supplementary control channel or a control channel useful in special circumstances.
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TL;DR: The brain's electrical signals enable people without muscle control to physically interact with the world through the use of their brains' electrical signals.
Abstract: The brain's electrical signals enable people without muscle control to physically interact with the world.
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"Controlling an arduino robot using ..." refers methods in this paper
...In our case, we send the output to an Arduino microcontroller which is used to control the movement of a servo motor [26]....
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