A Wearable SSVEP-Based BCI System for Quadcopter Control Using Head-Mounted Device

Wang, Meng; Li, Renjie; Zhang, Ruofan; Li, Guangye; Zhang, Dingguo

doi:10.1109/access.2018.2825378

Cited by 118 publications

(68 citation statements)

References 29 publications

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“…Within the robotic arm control implementations we have Perera et al (2017Perera et al ( , 2016; Ruhunage et al (2017a, b); Yang et al (2017); ; Pelayo et al (2018); Zhang et al (2018), several solutions are presented. According to Perera et al (2017), the researchers developed a BCI system to control a robotic arm that aids in people's feeding process.…”

Section: Review Of Eeg-ssvep Applicationmentioning

confidence: 99%

“…The information gathered in this analysis is organized in Table 5. Such table indicates Nakanishi et al (2017) and Wang et al (2018) have used BCI hardware made by NeuroScan (from different models). Similarly, OpenBCI and Emotiv EPOC also demonstrate to be a popular solution with 5 articles each.…”

Section: Bci Equipmentmentioning

confidence: 99%

“…Applicability of SSVEP-based brain-computer interfaces 4 O1, O2, Oz, POz for robot navigation in real environments Lee et al (2012) A brain-wave-actuated small robot car using ensemble empirical mode decomposition-based approach 1 Oz Li et al (2018) A human-vehicle collaborative simulated driving system based 13 T7, T8, P7, P8, FC3, FC4, on hybrid brain-computer interfaces and computer vision C3, C4, CP3, CP4, P3, P4, Oz Liu et al (2018) Design of a video feedback SSVEP-BCI system for car control based on improved MUSIC method 3 O1, Oz, O2 Turnip et al (2016) An application of online ANFIS classifier for wheelchair based brain computer interface 3 O1, O2, Pz Wang et al (2018) A Wearable SSVEP-Based BCI System for quadcopter Control Using Head-Mounted Device 6 PO3, PO4, O1, O2, C5, C6 Mistry et al (2018) An SSVEP based brain computer interface system to control electric wheelchairs 6 P3, Pz, P4, O1, Oz, O2 Nakanishi et al (2017) Enhancing detection of SSVEPs for a high-speed brain 10 Pz, PO5, PO3, POz, PO4, speller using task-related component analysis PO6, O1, Oz, O2, Cz Pelayo et al (2018) Brain-Computer Interface Controlled Robotic Arm to Improve 6 Not specified Quality of Life Peng et al (2016) Control of a nursing bed based on a hybrid brain-computer interface 8 P7, P3, Pz, P4, P8, O1, Oz, O2 Perera et al (2016) EEG-controlled meal assistance robot with camera-based automatic 8 O1, O2, POz, PO3, PO4, mouth position tracking and mouth open detection C1, C2, C3 Perera et al (2017) SSVEP Based BMI for a meal assistance robot 8 O1, O2, POz, PO3, PO4, C1, C2, C3 Zhao et al (2017) A SSVEP intelligent home service system based on CCA Not specified Not specified Ruhunage et al (2017) EMG signal controlled transhumeral prosthetic with 2 POz, Oz EEG-SSVEP based approach for hand open/close Savic and Popovic (2015) Brain computer interface prototypes for upper limb 3 C3, Oz, Cz Rehabilitation: a review of principles and experimental results 2017Brain-computer interface based stochastic navigation and control 4 PZ, O1, Oz, O2 of a semiautonomous mobile robot in an indoor environment Virdi et al (2017) Home automation control system implementation using 8 PO7, PO3, PO4, POz, PO8, SSVEP based brain-computer interface O1, O2, Oz Zhang et al (2016) A hybrid EEG-based BCI for robot grasp controlling 8 P7, P3, Pz, P4, P8, O1, Oz, O2 Won et al 2014A BCI Speller based on SSVEP using high frequency stimuli design 15 P5, P3, P1, Pz, P2, P4, P6, PO7, PO3, POz, PO4, PO8, O1, Oz, O2 Wu et al 2016A new SSVEP based BCI application on the mobile robot in a maze 4 O1, Oz, O2, Pz game Yang et al (2017) Mind control of a robotic arm with vis...…”

Section: Bci Equipmentmentioning

confidence: 99%

See 2 more Smart Citations

Review of Steady State Visually Evoked Potential Brain-Computer Interface Applications: Technological Analysis and Classification

Zavala¹,

López²,

Chicaiza³

et al. 2019

J. Eng. Appl. Sci.

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Brain-computer interface is a technology which creates a new way of communication between a person's brain and the external world. To achieve this objective, the brainwaves of a person must be gathered by using specialized devices and then classified in different categories that are associated with specific commands. In the process of brainwave gathering, brain activities of a person can be influenced by different types of stimuli to get the desired results and one of the most important and popular stimuli used in this field is steady state visually evoked potential. Based on this background, this review seeks to show and analyze a series of articles that have been executed around the world related to brain-computer interface applications using steady state visually evoked potential. This review has been executed with the objective of identifying the advantages and limitations of utilizing steady state visually evoked potentials, its main areas of application and the future challenges. Additionally, this review analyzes the different technologies involved to the implementation of state visually evoked potential systems such as signal classification techniques, electroencephalography devices, channels, verification metrics and experimental environments used in the research projects. In summary, this review intends to guide the scientific community about the different aspects involved in conducting research on the development of brain-computer interface applications using electroencephalography devices and steady state visually evoked potential.

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Section: Review Of Eeg-ssvep Applicationmentioning

confidence: 99%

Section: Bci Equipmentmentioning

confidence: 99%

Section: Bci Equipmentmentioning

confidence: 99%

See 1 more Smart Citation

Review of Steady State Visually Evoked Potential Brain-Computer Interface Applications: Technological Analysis and Classification

Zavala¹,

López²,

Chicaiza³

et al. 2019

J. Eng. Appl. Sci.

View full text Add to dashboard Cite

show abstract

“…EEG-based BCI has several paradigms for signal acquisition such as motor imagery (MI) [6]- [8], event-related potential (ERP) [9], [10], and movement-related cortical potential (MRCP) [2], [11]. As applications of EEGbased BCI, a robotic arm [6], [12], [13], a speller [4], [14]- [16], a wheelchair [17], and a drone [18]- [21] were commonly used for communication between human and machines. Recently, a BCI-based drone swarm is one of the most interesting topics ( Fig.…”

Section: Introductionmentioning

confidence: 99%

Towards Brain-Computer Interfaces for Drone Swarm Control

Jeong

Lee

Ahn

et al. 2020

2020 8th International Winter Conference on Brain-Computer Interface (BCI)

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Noninvasive brain-computer interface (BCI) decodes brain signals to understand user intention. Recent advances have been developed for the BCI-based drone control system as the demand for drone control increases. Especially, drone swarm control based on brain signals could provide various industries such as military service or industry disaster. This paper presents a prototype of a brain-swarm interface system for a variety of scenarios using a visual imagery paradigm. We designed the experimental environment that could acquire brain signals under a drone swarm control simulator environment. Through the system, we collected the electroencephalogram (EEG) signals with respect to four different scenarios. Seven subjects participated in our experiment and evaluated classification performances using the basic machine learning algorithm. The grand average classification accuracy is higher than the chance level accuracy. Hence, we could confirm the feasibility of the drone swarm control system based on EEG signals for performing high-level tasks.

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“…e Emotiv EPOC was utilized in the shooting game in which the subject could use their brain to control the direction of the pistol in the online target shooting [18]. e researchers designed a wearable BCI system based on SSVEP, which enabled 3-D navigation of quadcopter flight with immersive first-person visual feedback using a head-mounted device [19]. erefore, the Emotiv EPOC device is quite enough to be used in portable daily-life applications to improve life quality for healthy and disabled people [20].…”

Section: Introductionmentioning

confidence: 99%

Channel Projection-Based CCA Target Identification Method for an SSVEP-Based BCI System of Quadrotor Helicopter Control

Gao

Zhang

Wang

et al. 2019

Computational Intelligence and Neuroscience

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The brain-computer interface (BCI) plays an important role in assisting patients with amyotrophic lateral sclerosis (ALS) to enable them to participate in communication and entertainment. In this study, a novel channel projection-based canonical correlation analysis (CP-CCA) target identification method for steady-state visual evoked potential- (SSVEP-) based BCI system was proposed. The single-channel electroencephalography (EEG) signals of multiple trials were recorded when the subject is under the same stimulus frequency. The CCAs between single-channel EEG signals of multiple trials and sine-cosine reference signals were obtained. Then, the optimal reference signal of each channel was utilized to estimate the test EEG signal. To validate the proposed method, we acquired the training dataset with two testing conditions including the optimal time window length and the number of the trial of training data. The offline experiments conducted a comparison of the proposed method with the traditional canonical correlation analysis (CCA) and power spectrum density analysis (PSDA) method using a 5-class SSVEP dataset that was recorded from 10 subjects. Based on the training dataset, the online 3D-helicopter control experiment was carried out. The offline experimental results showed that the proposed method outperformed the CCA and the PSDA methods in terms of classification accuracy and information transfer rate (ITR). Furthermore, the online experiments of 3-DOF helicopter control achieved an average accuracy of 87.94 ± 5.93% with an ITR of 21.07 ± 4.42 bit/min.

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A Wearable SSVEP-Based BCI System for Quadcopter Control Using Head-Mounted Device

Cited by 118 publications

References 29 publications

Review of Steady State Visually Evoked Potential Brain-Computer Interface Applications: Technological Analysis and Classification

Review of Steady State Visually Evoked Potential Brain-Computer Interface Applications: Technological Analysis and Classification

Towards Brain-Computer Interfaces for Drone Swarm Control

Channel Projection-Based CCA Target Identification Method for an SSVEP-Based BCI System of Quadrotor Helicopter Control

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