Design of computer brain interface for flight control of unmanned air vehicle using cerebral signals through headset electroencephalograph

Mamani, Marco Antonio; Yanyachi, Pablo R.

doi:10.1109/incas.2017.8123499

Cited by 8 publications

(8 citation statements)

References 6 publications

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“…The brain wave regarding the winks has been captured using the fives channel Emotiv Insight EEG headset. Emotiv Insight has also been employed in other studies [14][15][16][17][18][19] to capture the brain wave. The remaining part of this paper has been organized in the following sections i.e.…”

Section: Introductionmentioning

confidence: 99%

Wink based facial expression classification using machine learning approach

et al. 2020

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Facial expression may establish communication between physically disabled people and assistive devices. Different types of facial expression including eye wink, smile, eye blink, looking up and looking down can be extracted from the brain signal. In this study, the possibility of controlling assistive devices using the individual's wink has been investigated. Brain signals from the five subjects have been captured to recognize the left wink, right wink, and no wink. The brain signals have been captured using Emotiv Insight which consists of five channels. Fast Fourier transform and the sample range have been computed to extract the features. The extracted features have been classified with the help of different machine learning algorithms. Here, support vector machine (SVM), linear discriminant analysis (LDA) and K-nearest neighbor (K-NN) have been employed to classify the features sets. The performance of the classifier in terms of accuracy, confusion matrix, true positive and false positive rate and the area under curve (AUC)-receiver operating characteristics (ROC) have been evaluated. In the case of sample range, the highest training and testing accuracies are 98.9% and 96.7% respectively which have been achieved by two classifiers namely, SVM and K-NN. The achieved results indicate that the person's wink can be utilized in controlling assistive devices.

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Section: Introductionmentioning

confidence: 99%

Wink based facial expression classification using machine learning approach

et al. 2020

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“…Mamani et al [23] used Emotiv Insight with five channels to command the movement of a BCI drone. To record, process, and classify the EEG data Emotiv software was employed.…”

Section: ) Motor Imagery (Mi)mentioning

confidence: 99%

“…The average number of EEG channels is 14.41. The fewest number of EEG sensors reported in these papers is five [22] [25] [23] followed by six [26] and eight [29]. The most used number of sensors is fourteen [31] [25] [33] [21] [24] and the most sensors used is Sixty-four [39].…”

Section: ) Statisticsmentioning

confidence: 99%

Brain-Computer Interface Controlled Drones: A Systematic Review

Glavas,

Tzimourta,

Angelidis

et al. 2024

IEEE Access

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The goal of this systematic review is to examine the use of Brain-Computer Interfaces (BCIs) for controlling unmanned aerial vehicles (UAVs) in real-time. A comprehensive search across various online databases, including IEEE Explore, ScienceDirect, MDPI and PubMed, using the PRISMA research method, was conducted. The total of 42 experimental studies were identified, analyzed, and included in the final report. The review highlights several important research directions and areas that require further investigation in the field. Additionally, it identifies potential future possibilities and trends in BCI-controlled UAVs. Lastly, the review outlines the future challenges that researchers are likely to encounter, aiming to provide valuable insights and guidance for future studies in this area. To the authors' best knowledge, this systematic review represents the most extensive analysis in literature, both in terms of the number of studies included and the span of years considered.

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“…Brain-Computer Interface is a multidisciplinary research field, which comprises achievements in related scientific and technical areas: artificial intelligence, computer science, mechatronics [1][2][3], signal processing, neuroscience, and psychology [4]. Beyond its various applications in non-clinical fields (digital games, sleep avoidance, mental states monitoring, advertisement, and business), the fundamental aim of a brain-computer interface system is related to helping people with neuromotor disabilities who cannot communicate with the outside environment by using natural paths, such as muscles and peripheral nerves.…”

Section: Introductionmentioning

confidence: 99%

A LabVIEW Instrument Aimed for the Research on Brain-Computer Interface by Enabling the Acquisition, Processing, and the Neural Networks based Classification of the Raw EEG Signal Detected by the Embedded NeuroSky Biosensor

Rușanu¹

2022

Preprint

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This paper proposes several LabVIEW applications to accomplish the data acquisition, processing, features extraction and real-time classification of the electroencephalographic (EEG) signal detected by the embedded sensor of the NeuroSky Mindwave Mobile headset. The LabVIEW applications are aimed at the implementation of a Brain-Computer Interface system, which is necessary to people with neuromotor disabilities. It is analyzed a novel approach regarding the preparation and automatic generation of the EEG dataset by identifying the most relevant multiple mixtures between selected EEG rhythms (both time and frequency domains of raw signal, delta, theta, alpha, beta, gamma) and extracted statistical features (mean, median, standard deviation, route mean square, Kurtosis coefficient and others). The acquired raw EEG signal is processed and segmented into temporal sequences corresponding to the detection of the multiple voluntary eye-blinks EEG patterns. The main LabVIEW application accomplished the optimal real-time artificial neural networks techniques for the classification of the EEG temporal sequences corresponding to the four states: 0 - No Eye-Blink Detected; 1 - One Eye-Blink Detected; 2 – Two Eye-Blinks Detected and 3 – Three Eye-Blinks Detected. Nevertheless, the application can be used to classify other EEG patterns corresponding to different cognitive tasks, since the whole functionality and working principle could estimate the labels associated with various classes.

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Design of computer brain interface for flight control of unmanned air vehicle using cerebral signals through headset electroencephalograph

Cited by 8 publications

References 6 publications

Wink based facial expression classification using machine learning approach

Wink based facial expression classification using machine learning approach

Brain-Computer Interface Controlled Drones: A Systematic Review

A LabVIEW Instrument Aimed for the Research on Brain-Computer Interface by Enabling the Acquisition, Processing, and the Neural Networks based Classification of the Raw EEG Signal Detected by the Embedded NeuroSky Biosensor

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