Proceedings ArticleDOI
Controlling an arduino robot using Brain Computer Interface
Parth Gargava,Karan Sindwani,Sumit Soman +2 more
- pp 1-5
TLDR
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.read more
Citations
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Proceedings ArticleDOI
Automated seed sowing agribot using arduino
Saurabh Umarkar,Anil Karwankar +1 more
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.
Posted ContentDOI
10 years of EPOC: A scoping review of Emotiv’s portable EEG device
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.
Proceedings ArticleDOI
Low complexity approach for controlling a robotic arm using the Emotiv EPOC headset
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.
Journal ArticleDOI
Automatic Floor Cleaning Robot Using Arduino and Ultrasonic Sensor
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.
Journal ArticleDOI
Measuring Biosignals with Single Circuit Boards
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.
References
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Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks
TL;DR: This article explores the use of scalar and multivariate autoregressive (AR) models to extract features from the human electroencephalogram (EEG) with which mental tasks can be discriminated, and investigates the feasibility of using EEG to allow paralyzed persons to control a device such as a wheelchair.
Journal ArticleDOI
Graz-BCI: state of the art and clinical applications
Gert Pfurtscheller,Christa Neuper,Gernot Müller,B. Obermaier,Gunther Krausz,Alois Schlögl,Reinhold Scherer,Bernhard Graimann,Claudia Keinrath,D. Skliris,M. Wortz,Gernot G. Supp,C. Schrank +12 more
TL;DR: Relevant clinical applications of BCI-based systems for control of a virtual keyboard device and operations of a hand orthosis are reported and it is demonstrated how information transfer rates can be acquired by real time classification of oscillatory activity.
Journal ArticleDOI
Robust classification of EEG signal for brain-computer interface
TL;DR: High accuracy, fast learning, and online performance make this P300 speller a potential communication tool for severely disabled individuals, who have lost all other means of communication and are otherwise cut off from the world, provided their disability does not interfere with the performance of the speller.
Journal ArticleDOI
The Berlin Brain-Computer Interface (BBCI) --- towards a new communication channel for online control in gaming applications
TL;DR: This contribution introduces the Berlin Brain–Computer Interface (BBCI) and presents setups where the user is provided with intuitive control strategies in plausible gaming applications that use biofeedback.
Journal ArticleDOI
A self-training semi-supervised SVM algorithm and its application in an EEG-based brain computer interface speller system
TL;DR: A self-training semi-supervised support vector machine (SVM) algorithm and its corresponding model selection method, which are designed to train a classifier with small training data are presented and the convergence of this algorithm is proved.