scholarly journals The Human—Unmanned Aerial Vehicle System Based on SSVEP—Brain Computer Interface

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 3025
Author(s):  
Ming-An Chung ◽  
Chia-Wei Lin ◽  
Chih-Tsung Chang
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Visual Stimulation ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Left Turn ◽  
Practical Applications ◽  
Aerial Vehicle ◽  
Time Required ◽  
Physically Challenged ◽  
The Brain

The brain–computer interface (BCI) is a mechanism for extracting information from the brain, with this information used for various applications. This study proposes a method to control an unmanned aerial vehicle (UAV) flying through a BCI system using the steady-state visual evoked potential (SSVEP) approach. The UAV’s screen emits three frequencies for visual stimulation: 15, 23, and 31 Hz for the UAV’s left-turn, forward-flight, and right-turn functions. Due to the requirement of immediate response to the UAV flight, this paper proposes a method to improve the accuracy rate and reduce the time required to correct instruction errors in the resolution of brainwave signals received by UAVs. This study tested ten subjects and verified that the proposed method has a 10% improvement inaccuracy. While the traditional method can take 8 s to correct an error, the proposed method requires only 1 s, making it more suitable for practical applications in UAVs. Furthermore, such a BCI application for UAV systems can achieve the same experience of using the remote control for physically challenged patients.

scholarly journals A BCI Based Alerting System for Attention Recovery of UAV Operators

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2447
Author(s):  
Jonghyuk Park ◽  
Jonghun Park ◽  
Dongmin Shin ◽  
Yerim Choi
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Brain Computer Interface ◽  
Statistical Significance ◽  
Computer Interface ◽  
Aerial Vehicles ◽  
Alerting System ◽  
Aerial Vehicle ◽  
Processing Module ◽  
The Brain ◽  
Signal Processing Module

As unmanned aerial vehicles have become popular, the number of accidents caused by an operator’s inattention have increased. To prevent such accidents, the operator should maintain an attention status. However, limited research has been conducted on the brain-computer interface (BCI)-based system with an alerting module for the operator’s attention recovery of unmanned aerial vehicles. Therefore, we introduce a detection and alerting system that prevents an unmanned aerial vehicle operator from falling into inattention status by using the operator’s electroencephalogram signal. The proposed system consists of the following three components: a signal processing module, which collects and preprocesses an electroencephalogram signal of an operator, an inattention detection module, which determines whether an inattention status occurred based on the preprocessed signal, and, lastly, an alert providing module that presents stimulus to an operator when inattention is detected. As a result of evaluating the performance with a real-world dataset, it was shown that the proposed system successfully contributed to the recovery of operator attention in the evaluating dataset, although statistical significance could not be established due to the small number of subjects.

scholarly journals Home Automation using Brain Computer Interface for the Physically Challenged

2019 ◽  
Vol 9 (1) ◽  
pp. 5018-5021
Keyword(s):  
Brain Computer Interface ◽  
Brain Structures ◽  
Computer Interface ◽  
Brain Machine Interface ◽  
Home Automation ◽  
Eeg Signals ◽  
Machine Interface ◽  
Physically Challenged ◽  
The Brain ◽  
Bci System

A Brain-Computer Interface (BCI)is labeledas Mind-Machine Interface (MMI) or a Brain-Machine Interface (BMI). It affords a non-muscular channel of messagein between the computer and a human brain. Using the enhancements in interface equipment to electronics,and the necessity to helpindividuals suffering from disabilities, a new area in this study has begun by acceptingtasks of brain. The Electro-Encephalogram (EEG) is an electrical activity created by brain structures and verified from the scalp using electrodes. The EEG signal is used in actualspell to accomplishperipheral devices using a broad BCI system. The post-processed output signals are converted to suitable instructions to regulate output devices. The main seek is to aidparalyzed and physically immobilizedpersons to govern the home appliances making use of Electro-Encephalogram (EEG) signals, such that they grow to beautonomous. According to the brain responsiveness the devices can be designated then usingrelays, the switching of the home-basedmachinescan be completedconsequently.

NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

2019 ◽  
pp. 139-145
Author(s):  
Selma Büyükgöze
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Electrode Placement ◽  
Eeg Signals ◽  
Non Invasive ◽  
Brain Signals ◽  
Brain States ◽  
The Brain

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

Classification of EEG Mental Patterns by Using Two Scalp Electrodes and Mahalanobis Distance-Based Classifiers

2002 ◽  
Vol 41 (04) ◽  
pp. 337-341 ◽  
Author(s):  
F. Cincotti ◽  
D. Mattia ◽  
C. Babiloni ◽  
F. Carducci ◽  
L. Bianchi ◽  
...  
Keyword(s):  
Mahalanobis Distance ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Correct Classification ◽  
Interface Area ◽  
System A ◽  
The Brain ◽  
Full Covariance Matrix ◽  
Recording Electrodes

Summary Objectives: In this paper, we explored the use of quadratic classifiers based on Mahalanobis distance to detect mental EEG patterns from a reduced set of scalp recording electrodes. Methods: Electrodes are placed in scalp centro-parietal zones (C3, P3, C4 and P4 positions of the international 10-20 system). A Mahalanobis distance classifier based on the use of full covariance matrix was used. Results: The quadratic classifier was able to detect EEG activity related to imagination of movement with an affordable accuracy (97% correct classification, on average) by using only C3 and C4 electrodes. Conclusions: Such a result is interesting for the use of Mahalanobis-based classifiers in the brain computer interface area.

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