An EEG-/EOG-Based Hybrid Brain-Computer Interface: Application on Controlling an Integrated Wheelchair Robotic Arm System

Huang, Qiyun; Zhang, Zhijun; Yu, Tao; He, Shenghong; Li, Yuanqing

doi:10.3389/fnins.2019.01243

Cited by 63 publications

(51 citation statements)

References 27 publications

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“…The low signal-to-noise ratio of EEG leads to low classification accuracy. Therefore, effective feature extraction and classification methods have become an important research topic of MI-EEG (Li et al, 2019). Commonly used feature extraction algorithms include wavelet transform (WT) (Xu et al, 2018), common spatial patterns (CSP) (Kumar et al, 2016), variations of CSP (Kim et al, 2016;Sakhavi and Guan, 2017), empirical mode decomposition (EMD) (Kevric and Subasi, 2017), and so on.…”

Section: Introductionmentioning

confidence: 99%

A Simplified CNN Classification Method for MI-EEG via the Electrode Pairs Signals

Lun

Chen

et al. 2020

Front. Hum. Neurosci.

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A brain-computer interface (BCI) based on electroencephalography (EEG) can provide independent information exchange and control channels for the brain and the outside world. However, EEG signals come from multiple electrodes, the data of which can generate multiple features. How to select electrodes and features to improve classification performance has become an urgent problem to be solved. This paper proposes a deep convolutional neural network (CNN) structure with separated temporal and spatial filters, which selects the raw EEG signals of the electrode pairs over the motor cortex region as hybrid samples without any preprocessing or artificial feature extraction operations. In the proposed structure, a 5-layer CNN has been applied to learn EEG features, a 4-layer max pooling has been used to reduce dimensionality, and a fully-connected (FC) layer has been utilized for classification. Dropout and batch normalization are also employed to reduce the risk of overfitting. In the experiment, the 4 s EEG data of 10, 20, 60, and 100 subjects from the Physionet database are used as the data source, and the motor imaginations (MI) tasks are divided into four types: left fist, right fist, both fists, and both feet. The results indicate that the global averaged accuracy on group-level classification can reach 97.28%, the area under the receiver operating characteristic (ROC) curve stands out at 0.997, and the electrode pair with the highest accuracy on 10 subjects dataset is FC3-FC4, with 98.61%. The research results also show that this CNN classification method with minimal (2) electrode can obtain high accuracy, which is an advantage over other methods on the same database. This proposed approach provides a new idea for simplifying the design of BCI systems, and accelerates the process of clinical application.

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Section: Introductionmentioning

confidence: 99%

A Simplified CNN Classification Method for MI-EEG via the Electrode Pairs Signals

Lun

Chen

et al. 2020

Front. Hum. Neurosci.

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“…Wang et al combined MI,P300, and EOG signals to asynchronously control a wheelchair (Wang et al, 2014). Huang et al used EOG for button selection, MI for directional control, and combined computer vision for the control of an integrated wheelchair robotic arm system (Huang et al, 2019). Tan et al applied autoencoder-based transfer learning in hybrid BCI for rehabilitation robot which composed of MI-based rehabilitation action, SSVEP-based menu selection, and EOG-based operation confirmation of cancellation (Tan et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

A Hybrid BCI Based on SSVEP and EOG for Robotic Arm Control

Zhu

Liu

et al. 2020

Front. Neurorobot.

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Brain-computer interface (BCI) for robotic arm control has been studied to improve the life quality of people with severe motor disabilities. There are still challenges for robotic arm control in accomplishing a complex task with a series of actions. An efficient switch and a timely cancel command are helpful in the application of robotic arm. Based on the above, we proposed an asynchronous hybrid BCI in this study. The basic control of a robotic arm with six degrees of freedom was a steady-state visual evoked potential (SSVEP) based BCI with fifteen target classes. We designed an EOG-based switch which used a triple blink to either activate or deactivate the flash of SSVEP-based BCI. Stopping flash in the idle state can help to reduce visual fatigue and false activation rate (FAR). Additionally, users were allowed to cancel the current command simply by a wink in the feedback phase to avoid executing the incorrect command. Fifteen subjects participated and completed the experiments. The cue-based experiment obtained an average accuracy of 92.09%, and the information transfer rates (ITR) resulted in 35.98 bits/min. The mean FAR of the switch was 0.01/min. Furthermore, all subjects succeeded in asynchronously operating the robotic arm to grasp, lift, and move a target object from the initial position to a specific location. The results indicated the feasibility of the combination of EOG and SSVEP signals and the flexibility of EOG signal in BCI to complete a complicated task of robotic arm control.

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“…In [ 37 ], the authors designed a BCI system to control a real-time wheelchair. The authors in [ 38 ] built a BCI system that successfully integrates a wheelchair with artificial limbs. They used the common spatial patterns (CSP) method to extract features and an SVM classifier.…”

Section: Introductionmentioning

confidence: 99%

“…They used the common spatial patterns (CSP) method to extract features and an SVM classifier. Although the system proposed in [ 38 ] was built to perform an everyday task, no feature selection was used to reduce the execution time. Edla et al [ 39 ] proposed a system to control wheelchairs based on wavelet packet decomposition (WPD).…”

Section: Introductionmentioning

confidence: 99%

A BCI System Based on Motor Imagery for Assisting People with Motor Deficiencies in the Limbs

et al. 2020

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Motor deficiencies constitute a significant problem affecting millions of people worldwide. Such people suffer from a debility in daily functioning, which may lead to decreased and incoherence in daily routines and deteriorate their quality of life (QoL). Thus, there is an essential need for assistive systems to help those people achieve their daily actions and enhance their overall QoL. This study proposes a novel brain–computer interface (BCI) system for assisting people with limb motor disabilities in performing their daily life activities by using their brain signals to control assistive devices. The extraction of useful features is vital for an efficient BCI system. Therefore, the proposed system consists of a hybrid feature set that feeds into three machine-learning (ML) classifiers to classify motor Imagery (MI) tasks. This hybrid feature selection (FS) system is practical, real-time, and an efficient BCI with low computation cost. We investigate different combinations of channels to select the combination that has the highest impact on performance. The results indicate that the highest achieved accuracies using a support vector machine (SVM) classifier are 93.46% and 86.0% for the BCI competition III–IVa dataset and the autocalibration and recurrent adaptation dataset, respectively. These datasets are used to test the performance of the proposed BCI. Also, we verify the effectiveness of the proposed BCI by comparing its performance with recent studies. We show that the proposed system is accurate and efficient. Future work can apply the proposed system to individuals with limb motor disabilities to assist them and test their capability to improve their QoL. Moreover, the forthcoming work can examine the system’s performance in controlling assistive devices such as wheelchairs or artificial limbs.

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An EEG-/EOG-Based Hybrid Brain-Computer Interface: Application on Controlling an Integrated Wheelchair Robotic Arm System

Cited by 63 publications

References 27 publications

A Simplified CNN Classification Method for MI-EEG via the Electrode Pairs Signals

A Simplified CNN Classification Method for MI-EEG via the Electrode Pairs Signals

A Hybrid BCI Based on SSVEP and EOG for Robotic Arm Control

A BCI System Based on Motor Imagery for Assisting People with Motor Deficiencies in the Limbs

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