Detection of Movement Intention in EEG-Based Brain-Computer Interfaces Using Fourier-Based Synchrosqueezing Transform

2021 ◽  
Author(s):  
Nedime Karakullukcu ◽  
Bülent Yilmaz
Keyword(s):  
Statistical Significance ◽  
Feature Selection Method ◽  
Support Vector ◽  
Brain Computer Interfaces ◽  
Motor Impairments ◽  
Computer Interfaces ◽  
Synchrosqueezing Transform ◽  
Feature Extractor ◽  
Processing Effort ◽  
Movement Intention

Patients with motor impairments need caregivers’ help to initiate the operation of brain-computer interfaces (BCI). This study aims to identify and characterize movement intention using multichannel electroencephalography (EEG) signals as a means to initiate BCI systems without extra accessories/methodologies. We propose to discriminate the resting and motor imagery (MI) states with high accuracy using Fourier-based synchrosqueezing transform (FSST) as a feature extractor. FSST has been investigated and compared with other popular approaches in 28 healthy subjects for a total of 6657 trials. The accuracy and f-measure values were obtained as 99.8% and 0.99, respectively, when FSST was used as the feature extractor and singular value decomposition (SVD) as the feature selection method and support vector machines as the classifier. Moreover, this study investigated the use of data that contain certain amount of noise without any preprocessing in addition to the clean counterparts. Furthermore, the statistical analysis of EEG channels with the best discrimination (of resting and MI states) characteristics demonstrated that F4-Fz-C3-Cz-C4-Pz channels and several statistical features had statistical significance levels, [Formula: see text], less than 0.05. This study showed that the preparation of the movement can be detected in real-time employing FSST-SVD combination and several channels with minimal pre-processing effort.

scholarly journals Improvement of P300-Based Brain–Computer Interfaces for Home Appliances Control by Data Balancing Techniques

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5576
Author(s):  
Taejun Lee ◽  
Minju Kim ◽  
Sung-Phil Kim
Keyword(s):  
Information Transfer ◽  
Regularization Parameter ◽  
Support Vector ◽  
Oddball Paradigm ◽  
Svm Classifier ◽  
Sampling Techniques ◽  
Brain Computer Interfaces ◽  
Data Imbalance ◽  
Imbalance Problem ◽  
Computer Interfaces

The oddball paradigm used in P300-based brain–computer interfaces (BCIs) intrinsically poses the issue of data imbalance between target stimuli and nontarget stimuli. Data imbalance can cause overfitting problems and, consequently, poor classification performance. The purpose of this study is to improve BCI performance by solving this data imbalance problem with sampling techniques. The sampling techniques were applied to BCI data in 15 subjects controlling a door lock, 15 subjects an electric light, and 14 subjects a Bluetooth speaker. We explored two categories of sampling techniques: oversampling and undersampling. Oversampling techniques, including random oversampling, synthetic minority oversampling technique (SMOTE), borderline-SMOTE, support vector machine (SVM) SMOTE, and adaptive synthetic sampling, were used to increase the number of samples for the class of target stimuli. Undersampling techniques, including random undersampling, neighborhood cleaning rule, Tomek’s links, and weighted undersampling bagging, were used to reduce the class size of nontarget stimuli. The over- or undersampled data were classified by an SVM classifier. Overall, some oversampling techniques improved BCI performance while undersampling techniques often degraded performance. Particularly, using borderline-SMOTE yielded the highest accuracy (87.27%) and information transfer rate (8.82 bpm) across all three appliances. Moreover, borderline-SMOTE led to performance improvement, especially for poor performers. A further analysis showed that borderline-SMOTE improved SVM by generating more support vectors within the target class and enlarging margins. However, there was no difference in the accuracy between borderline-SMOTE and the method of applying the weighted regularization parameter of the SVM. Our results suggest that although oversampling improves performance of P300-based BCIs, it is not just the effect of the oversampling techniques, but rather the effect of solving the data imbalance problem.

scholarly journals EEG Headset Evaluation for Detection of Single-Trial Movement Intention for Brain-Computer Interfaces

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2804 ◽  
Author(s):  
Mads Jochumsen ◽  
Hendrik Knoche ◽  
Troels Wesenberg Kjaer ◽  
Birthe Dinesen ◽  
Preben Kidmose
Keyword(s):  
Motor Cortex ◽  
Signal To Noise Ratio ◽  
Brain Computer Interfaces ◽  
Single Trial ◽  
Signal To Noise ◽  
Computer Interfaces ◽  
Continuous Eeg ◽  
Good For ◽  
Movement Intention ◽  
Healthy Participants

Brain–computer interfaces (BCIs) can be used in neurorehabilitation; however, the literature about transferring the technology to rehabilitation clinics is limited. A key component of a BCI is the headset, for which several options are available. The aim of this study was to test four commercially available headsets’ ability to record and classify movement intentions (movement-related cortical potentials—MRCPs). Twelve healthy participants performed 100 movements, while continuous EEG was recorded from the headsets on two different days to establish the reliability of the measures: classification accuracies of single-trials, number of rejected epochs, and signal-to-noise ratio. MRCPs could be recorded with the headsets covering the motor cortex, and they obtained the best classification accuracies (73%−77%). The reliability was moderate to good for the best headset (a gel-based headset covering the motor cortex). The results demonstrate that, among the evaluated headsets, reliable recordings of MRCPs require channels located close to the motor cortex and potentially a gel-based headset.

Visual P300 Mind-Speller Brain-Computer Interfaces: A Walk Through the Recent Developments With Special Focus on Classification Algorithms

2019 ◽  
Vol 51 (1) ◽  
pp. 19-33 ◽  
Author(s):  
Jobin T. Philip ◽  
S. Thomas George
Keyword(s):  
Information Transfer ◽  
Performance Metrics ◽  
Support Vector ◽  
Classification Algorithms ◽  
Special Focus ◽  
Brain Computer Interfaces ◽  
Human Beings ◽  
Computer Interfaces ◽  
Recent Developments ◽  
Visual P300

Brain-computer interfaces are sophisticated signal processing systems, which directly operate on neuronal signals to identify specific human intents. These systems can be applied to overcome certain disabilities or to enhance the natural capabilities of human beings. The visual P300 mind-speller is a prominent one among them, which has opened up tremendous possibilities in movement and communication applications. Today, there exist many state-of-the-art visual P300 mind-speller implementations in the literature as a result of numerous researches in this domain over the past 2 decades. Each of these systems can be evaluated in terms of performance metrics like classification accuracy, information transfer rate, and processing time. Various classification techniques associated with these systems, which include but are not limited to discriminant analysis, support vector machine, neural network, distance-based and ensemble of classifiers, have major roles in determining the overall system performances. The significance of a proper review on the recent developments in visual P300 mind-spellers with proper emphasis on their classification algorithms is the key insight for this work. This article is organized with a brief introduction to P300, concepts of visual P300 mind-spellers, the survey of literature with special focus on classification algorithms, followed by the discussion of various challenges and future directions.

Motor Imagery EEG Recognition Based on WPD-CSP and KF-SVM in Brain Computer Interfaces

2014 ◽  
Vol 556-562 ◽  
pp. 2829-2833 ◽  
Author(s):  
Bang Hua Yang ◽  
Ting Wu ◽  
Qian Wang ◽  
Zhi Jun Han
Keyword(s):  
Feature Extraction ◽  
Motor Imagery ◽  
Kernel Function ◽  
Feature Vector ◽  
Wavelet Packet ◽  
Support Vector ◽  
Wavelet Packet Decomposition ◽  
Brain Computer Interfaces ◽  
Recognition Method ◽  
Computer Interfaces

A recognition method based on Wavelet Packet Decomposition - Common Spatial Patterns (WPD-CSP) and Kernel Fisher Support Vector Machine (KF-SVM) is developed and used for EEG recognition in motor imagery brain–computer interfaces (BCIs). The WPD-CSP is used for feature extraction and KF-SVM is used for classification. The presented recognition method includes the following steps: (1) some important EEG channels are selected. The 'haar' wavelet basis is used to take wavelet packet decomposition. And some decomposed sub-bands related with motor imagery for each EEG channel are reconstructed to obtain the relevant frequency information. (2) A six-dimensional feature vector is obtained by the CSP feature extraction to the reconstructed signal. And then the within-class scatter is calculated based on the feature vector. (3) The scatter is added into the radical basis function to construct a new kernel function. The obtained new kernel is integrated into the SVM to act as its kernel function. To evaluate effectiveness of the proposed WPD-CSP + KF-SVM method, the data from the 2008 international BCI competition are processed. A preliminary result shows that the proposed classification algorithm can well recognize EEG data and improve the EEG recognition accuracy in motor imagery BCIs.

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