Mind Controlled Drone: An Innovative Multiclass SSVEP based Brain Computer Interface

Chiuzbaian, Andrei; Jakobsen, J. N.; Puthusserypady, Sadasivan

doi:10.1109/iww-bci.2019.8737327

Cited by 29 publications

(27 citation statements)

References 18 publications

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“…Engineering : This is the second highest research area in this SLR, with 24 articles and 9 companies. Eight articles used Emotiv equipment [ 5 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ], five articles used g.Tec equipment [ 51 , 52 , 53 , 54 , 55 ], three articles used Brain Products equipment [ 56 , 57 , 58 ], two articles used Compumedics Neuroscan equipment [ 59 , 60 ], and two articles used NeuroSky equipment [ 61 , 62 ]. For the equipment for Cognionics Inc. [ 63 ], Electrical Geodesics Inc. [ 64 ], Neuroelectrics [ 65 ], and Advanced Brain Monitoring [ 66 ], one article each used their equipment.…”

Section: Resultsmentioning

confidence: 99%

“…The prospect of BCI is applicable to both indoor and outdoor applications. The outdoor applications include helping security teams explore opponent areas for safety and security [ 50 , 276 ]. Other innovations using the BCI technology influence researchers to use BCIs as a part of daily life, such as controlling vehicles for assistance in remote driving [ 49 ].…”

Section: Discussionmentioning

confidence: 99%

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Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review

Jamil

Belkacem

Ouhbi

et al. 2021

Sensors

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Humans interact with computers through various devices. Such interactions may not require any physical movement, thus aiding people with severe motor disabilities in communicating with external devices. The brain–computer interface (BCI) has turned into a field involving new elements for assistive and rehabilitative technologies. This systematic literature review (SLR) aims to help BCI investigator and investors to decide which devices to select or which studies to support based on the current market examination. This examination of noninvasive EEG devices is based on published BCI studies in different research areas. In this SLR, the research area of noninvasive BCIs using electroencephalography (EEG) was analyzed by examining the types of equipment used for assistive, adaptive, and rehabilitative BCIs. For this SLR, candidate studies were selected from the IEEE digital library, PubMed, Scopus, and ScienceDirect. The inclusion criteria (IC) were limited to studies focusing on applications and devices of the BCI technology. The data used herein were selected using IC and exclusion criteria to ensure quality assessment. The selected articles were divided into four main research areas: education, engineering, entertainment, and medicine. Overall, 238 papers were selected based on IC. Moreover, 28 companies were identified that developed wired and wireless equipment as means of BCI assistive technology. The findings of this review indicate that the implications of using BCIs for assistive, adaptive, and rehabilitative technologies are encouraging for people with severe motor disabilities and healthy people. With an increasing number of healthy people using BCIs, other research areas, such as the motivation of players when participating in games or the security of soldiers when observing certain areas, can be studied and collaborated using the BCI technology. However, such BCI systems must be simple (wearable), convenient (sensor fabrics and self-adjusting abilities), and inexpensive.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review

Jamil

Belkacem

Ouhbi

et al. 2021

Sensors

View full text Add to dashboard Cite

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“…The BCI extracts data from electrodes or sensors acquiring neural signals and translates those data into digital code [1]. Application of BCI range from ones focused on improved health outcomes (i.e., rehabilitation of impaired motor function [2], restoring sensory functions [3], or interpreting thoughts from individuals who cannot otherwise communicate them [5]), enhanced control of devices (i.e., operating heavy machinery, flying drones [6], or driving [7]), and even recreational uses (i.e., gaming [8]). Invasive BCIs that access raw neural signals have recently shown high accuracy in interpreting human/animal intentions, actions, and imagery [3,5].…”

Section: Introductionmentioning

confidence: 99%

Brain-Computer Interface using temporal- spectral features and neural network classifier

Cerf¹,

Wang²

2022

Preprint

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<div> <div> <div> <p>We show that by extracting temporal and spectral features from EEG signal and, following, using neural network to classify those features, one can significantly improve the performance of Brain-Computer Interfaces (BCIs) in predicting which motor movement was imagined by a subject. Our movement prediction algorithm uses Sequential Backward Selection technique to jointly select the temporal and spectral features, and a radial basis function neural network for the classification. The method shows an average performance increase of 5.96% compared to state-of-the-art benchmark algorithms. Using two popular public datasets, our algorithm reaches 91.73% accuracy (compared to an average benchmark of 81.10%) on the first dataset, and 88.78% (average benchmark: 82.76%) on the second dataset. Given the high variability within- and across-subjects in EEG-based motion decoding, we suggest that using features from multiple modalities along with neural network feature selection and classification protocol is likely to increase BCI performance across various tasks. </p> </div> </div> </div>

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“…An increasing number of systems allow the control of more sophisticated devices, including orthoses, prostheses, drones, robotic arms or even mobile robots. BCI systems can also be used for neurorehabilitation [42][43][44][45].…”

Section: Introductionmentioning

confidence: 99%

Using Brain-Computer Interface to Control a Virtual Drone Using Non-Invasive Motor Imagery and Machine Learning

2021

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In recent years, the control of devices “by the power of the mind” has become a very controversial topic but has also been very well researched in the field of state-of-the-art gadgets, such as smartphones, laptops, tablets and even smart TVs, and also in medicine, to be used by people with disabilities for whom these technologies may be the only way to communicate with the outside world. It is well known that BCI control is a skill and can be improved through practice and training. This paper aims to improve and diversify signal processing methods for the implementation of a brain-computer interface (BCI) based on neurological phenomena recorded during motor tasks using motor imagery (MI). The aim of the research is to extract, select and classify the characteristics of electroencephalogram (EEG) signals, which are based on sensorimotor rhythms, for the implementation of BCI systems. This article investigates systems based on brain-computer interfaces, especially those that use the electroencephalogram as a method of acquisition of MI tasks. The purpose of this article is to allow users to manipulate quadcopter virtual structures (external, robotic objects) simply through brain activity, correlated with certain mental tasks using undecimal transformation (UWT) to reduce noise, Independent Component Analysis (ICA) together with determination coefficient (r2) and, for classification, a hybrid neural network consisting of Radial Basis Functions (RBF) and a multilayer perceptron–recurrent network (MLP–RNN), obtaining a classification accuracy of 95.5%. Following the tests performed, it can be stated that the use of biopotentials in human–computer interfaces is a viable method for applications in the field of BCI. The results presented show that BCI training can produce a rapid change in behavioral performance and cognitive properties. If more than one training session is used, the results may be beneficial for increasing poor cognitive performance. To achieve this goal, three steps were taken: understanding the functioning of BCI systems and the neurological phenomena involved; acquiring EEG signals based on sensorimotor rhythms recorded during MI tasks; applying and optimizing extraction methods, selecting and classifying characteristics using neuronal networks.

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Mind Controlled Drone: An Innovative Multiclass SSVEP based Brain Computer Interface

Cited by 29 publications

References 18 publications

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review

Brain-Computer Interface using temporal- spectral features and neural network classifier

Using Brain-Computer Interface to Control a Virtual Drone Using Non-Invasive Motor Imagery and Machine Learning

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