Crossing time windows optimization based on mutual information for hybrid BCI

<abstract> <p>Hybrid EEG-fNIRS brain-computer interface (HBCI) is widely employed to enhance BCI performance. EEG and fNIRS signals are combined to increase the dimensionality of the information. Time windows are used to select EEG and fNIRS singles synchronously. However, it ignores that specific modal signals have their own characteristics, when the task is stimulated, the information between the modalities will mismatch at the moment, which has a significant impact on the classification performance. Here we propose a novel crossing time windows optimization for mental arithmetic (MA) based BCI. The EEG and fNIRS signals were segmented separately by sliding time windows. Then crossing time windows (CTW) were combined with each one segment from EEG and fNIRS selected independently. Furthermore, EEG and fNIRS features were extracted using Filter Bank Common Spatial Pattern (FBCSP) and statistical methods from each sample. Mutual information was calculated for FBCSP and statistical features to characterize the discrimination of crossing time windows, and the optimal window would be selected based on the largest mutual information. Finally, a sparse structured framework of Fisher Lasso feature selection (FLFS) was designed to select the joint features, and conventional Linear Discriminant Analysis (LDA) was employed to perform classification. We used proposed method for a MA dataset. The classification accuracy of the proposed method is 92.52 ± 5.38% and higher than other methods, which shows the rationality and superiority of the proposed method.</p> </abstract>

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Mental Task Evaluation for Hybrid NIRS-EEG Brain-Computer Interfaces

Computational Intelligence and Neuroscience ◽

10.1155/2017/3524208 ◽

2017 ◽

Vol 2017 ◽

pp. 1-24 ◽

Cited By ~ 9

Author(s):

Hubert Banville ◽

Rishabh Gupta ◽

Tiago H. Falk

Keyword(s):

Near Infrared ◽

Mental Arithmetic ◽

Binary Classification ◽

Time Windows ◽

Brain Regions ◽

Classification Performance ◽

Response Patterns ◽

Word Generation ◽

Nirs Data ◽

Accuracy Increase

Based on recent electroencephalography (EEG) and near-infrared spectroscopy (NIRS) studies that showed that tasks such as motor imagery and mental arithmetic induce specific neural response patterns, we propose a hybrid brain-computer interface (hBCI) paradigm in which EEG and NIRS data are fused to improve binary classification performance. We recorded simultaneous NIRS-EEG data from nine participants performing seven mental tasks (word generation, mental rotation, subtraction, singing and navigation, and motor and face imagery). Classifiers were trained for each possible pair of tasks using (1) EEG features alone, (2) NIRS features alone, and (3) EEG and NIRS features combined, to identify the best task pairs and assess the usefulness of a multimodal approach. The NIRS-EEG approach led to an average increase in peak kappa of 0.03 when using features extracted from one-second windows (equivalent to an increase of 1.5% in classification accuracy for balanced classes). The increase was much stronger (0.20, corresponding to an 10% accuracy increase) when focusing on time windows of high NIRS performance. The EEG and NIRS analyses further unveiled relevant brain regions and important feature types. This work provides a basis for future NIRS-EEG hBCI studies aiming to improve classification performance toward more efficient and flexible BCIs.

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Comparing Neural Correlates of Human Emotions across Multiple Stimulus Presentation Paradigms

Brain Sciences ◽

10.3390/brainsci11060696 ◽

2021 ◽

Vol 11 (6) ◽

pp. 696

Author(s):

Naveen Masood ◽

Humera Farooq

Keyword(s):

Emotion Recognition ◽

Stimulus Presentation ◽

Neural Correlates ◽

Classification Performance ◽

Emotional States ◽

Common Spatial Pattern ◽

Spatial Filters ◽

Linear Discriminant ◽

Spectral Bands ◽

Eeg Data

Most electroencephalography (EEG)-based emotion recognition systems rely on a single stimulus to evoke emotions. These systems make use of videos, sounds, and images as stimuli. Few studies have been found for self-induced emotions. The question “if different stimulus presentation paradigms for same emotion, produce any subject and stimulus independent neural correlates” remains unanswered. Furthermore, we found that there are publicly available datasets that are used in a large number of studies targeting EEG-based human emotional state recognition. Since one of the major concerns and contributions of this work is towards classifying emotions while subjects experience different stimulus-presentation paradigms, we need to perform new experiments. This paper presents a novel experimental study that recorded EEG data for three different human emotional states evoked with four different stimuli presentation paradigms. Fear, neutral, and joy have been considered as three emotional states. In this work, features were extracted with common spatial pattern (CSP) from recorded EEG data and classified through linear discriminant analysis (LDA). The considered emotion-evoking paradigms included emotional imagery, pictures, sounds, and audio–video movie clips. Experiments were conducted with twenty-five participants. Classification performance in different paradigms was evaluated, considering different spectral bands. With a few exceptions, all paradigms showed the best emotion recognition for higher frequency spectral ranges. Interestingly, joy emotions were classified more strongly as compared to fear. The average neural patterns for fear vs. joy emotional states are presented with topographical maps based on spatial filters obtained with CSP for averaged band power changes for all four paradigms. With respect to the spectral bands, beta and alpha oscillation responses produced the highest number of significant results for the paradigms under consideration. With respect to brain region, the frontal lobe produced the most significant results irrespective of paradigms and spectral bands. The temporal site also played an effective role in generating statistically significant findings. To the best of our knowledge, no study has been conducted for EEG emotion recognition while considering four different stimuli paradigms. This work provides a good contribution towards designing EEG-based system for human emotion recognition that could work effectively in different real-time scenarios.

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EEG Classification for BCI Based on CSP and SVM-GA

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.459.228 ◽

2013 ◽

Vol 459 ◽

pp. 228-231 ◽

Cited By ~ 4

Author(s):

Hao Yang ◽

Song Wu

Keyword(s):

Classification Performance ◽

Computer Interface ◽

Support Vector ◽

Eeg Signals ◽

Common Spatial Pattern ◽

Data Set ◽

Linear Discriminant ◽

Brain Signals ◽

Electroencephalogram Eeg ◽

The Brain

Electroencephalogram (EEG) is generally used in Brain-Computer Interface (BCI) applications to measure the brain signals. However, the multichannel EEG signals characterized by unrelated and redundant features will deteriorate the classification accuracy. This paper presents a method based on common spatial pattern (CSP) for feature extraction and support vector machine with genetic algorithm (SVM-GA) as a classifier, the GA is used to optimize the kernel parameters setting. The proposed algorithm is performed on data set Iva of BCI Competition III. Results show that the proposed method outperforms the conventional linear discriminant analysis (LDA) in average classification performance.

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Analysis of Different Classification Techniques for Two-Class Functional Near-Infrared Spectroscopy-Based Brain-Computer Interface

Computational Intelligence and Neuroscience ◽

10.1155/2016/5480760 ◽

2016 ◽

Vol 2016 ◽

pp. 1-11 ◽

Cited By ~ 57

Author(s):

Noman Naseer ◽

Nauman Khalid Qureshi ◽

Farzan Majeed Noori ◽

Keum-Shik Hong

Keyword(s):

Infrared Spectroscopy ◽

Discriminant Analysis ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Continuous Wave ◽

Mental Arithmetic ◽

Statistical Significance ◽

Support Vector ◽

Functional Near Infrared Spectroscopy ◽

Linear Discriminant

We analyse and compare the classification accuracies of six different classifiers for a two-class mental task (mental arithmetic and rest) using functional near-infrared spectroscopy (fNIRS) signals. The signals of the mental arithmetic and rest tasks from the prefrontal cortex region of the brain for seven healthy subjects were acquired using a multichannel continuous-wave imaging system. After removal of the physiological noises, six features were extracted from the oxygenated hemoglobin (HbO) signals. Two- and three-dimensional combinations of those features were used for classification of mental tasks. In the classification, six different modalities, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA),k-nearest neighbour (kNN), the Naïve Bayes approach, support vector machine (SVM), and artificial neural networks (ANN), were utilized. With these classifiers, the average classification accuracies among the seven subjects for the 2- and 3-dimensional combinations of features were 71.6, 90.0, 69.7, 89.8, 89.5, and 91.4% and 79.6, 95.2, 64.5, 94.8, 95.2, and 96.3%, respectively. ANN showed the maximum classification accuracies: 91.4 and 96.3%. In order to validate the results, a statistical significance test was performed, which confirmed that thepvalues were statistically significant relative to all of the other classifiers (p< 0.005) using HbO signals.

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A New Approach to Fall Detection Based on Improved Dual Parallel Channels Convolutional Neural Network

Sensors ◽

10.3390/s19122814 ◽

2019 ◽

Vol 19 (12) ◽

pp. 2814 ◽

Cited By ~ 2

Author(s):

Xiaoguang Liu ◽

Huanliang Li ◽

Cunguang Lou ◽

Tie Liang ◽

Xiuling Liu ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Nearest Neighbor ◽

Fall Detection ◽

Classification Performance ◽

Daily Activities ◽

Support Vector ◽

K Nearest Neighbor ◽

Linear Discriminant ◽

Parallel Channels

Falls are the major cause of fatal and non-fatal injury among people aged more than 65 years. Due to the grave consequences of the occurrence of falls, it is necessary to conduct thorough research on falls. This paper presents a method for the study of fall detection using surface electromyography (sEMG) based on an improved dual parallel channels convolutional neural network (IDPC-CNN). The proposed IDPC-CNN model is designed to identify falls from daily activities using the spectral features of sEMG. Firstly, the classification accuracy of time domain features and spectrograms are compared using linear discriminant analysis (LDA), k-nearest neighbor (KNN) and support vector machine (SVM). Results show that spectrograms provide a richer way to extract pattern information and better classification performance. Therefore, the spectrogram features of sEMG are selected as the input of IDPC-CNN to distinguish between daily activities and falls. Finally, The IDPC-CNN is compared with SVM and three different structure CNNs under the same conditions. Experimental results show that the proposed IDPC-CNN achieves 92.55% accuracy, 95.71% sensitivity and 91.7% specificity. Overall, The IDPC-CNN is more effective than the comparison in accuracy, efficiency, training and generalization.

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Enhancing Classification Performance of fNIRS-BCI by Identifying Cortically Active Channels Using the z-Score Method

Sensors ◽

10.3390/s20236995 ◽

2020 ◽

Vol 20 (23) ◽

pp. 6995

Author(s):

Hammad Nazeer ◽

Noman Naseer ◽

Aakif Mehboob ◽

Muhammad Jawad Khan ◽

Rayyan Azam Khan ◽

...

Keyword(s):

Open Access ◽

Motor Imagery ◽

Mental Arithmetic ◽

State Of The Art ◽

Brain Activity ◽

Channel Selection ◽

Classification Performance ◽

Finger Tapping ◽

Z Score ◽

Score Method

A state-of-the-art brain–computer interface (BCI) system includes brain signal acquisition, noise removal, channel selection, feature extraction, classification, and an application interface. In functional near-infrared spectroscopy-based BCI (fNIRS-BCI) channel selection may enhance classification performance by identifying suitable brain regions that contain brain activity. In this study, the z-score method for channel selection is proposed to improve fNIRS-BCI performance. The proposed method uses cross-correlation to match the similarity between desired and recorded brain activity signals, followed by forming a vector of each channel’s correlation coefficients’ maximum values. After that, the z-score is calculated for each value of that vector. A channel is selected based on a positive z-score value. The proposed method is applied to an open-access dataset containing mental arithmetic (MA) and motor imagery (MI) tasks for twenty-nine subjects. The proposed method is compared with the conventional t-value method and with no channel selected, i.e., using all channels. The z-score method yielded significantly improved (p < 0.0167) classification accuracies of 87.2 ± 7.0%, 88.4 ± 6.2%, and 88.1 ± 6.9% for left motor imagery (LMI) vs. rest, right motor imagery (RMI) vs. rest, and mental arithmetic (MA) vs. rest, respectively. The proposed method is also validated on an open-access database of 17 subjects, containing right-hand finger tapping (RFT), left-hand finger tapping (LFT), and dominant side foot tapping (FT) tasks.The study shows an enhanced performance of the z-score method over the t-value method as an advancement in efforts to improve state-of-the-art fNIRS-BCI systems’ performance.

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Acoustic Discrimination of Pathological Voice

Journal of Speech Language and Hearing Research ◽

10.1044/1092-4388(2001/027) ◽

2001 ◽

Vol 44 (2) ◽

pp. 327-339 ◽

Cited By ~ 175

Author(s):

Vijay Parsa ◽

Donald G. Jamieson

Keyword(s):

Operating Characteristic ◽

Classification Performance ◽

Spectral Measures ◽

Continuous Speech ◽

Roc Curve Analysis ◽

Linear Discriminant ◽

Acoustic Measures ◽

Acoustic Discrimination ◽

Pathological Voice

We investigated the ability of acoustic measures to discriminate between normal and pathological talkers. Two groups of measures were compared: (a) those extracted from sustained vowels and (b) those based on continuous speech samples. Nine acoustic measures, which include fundamental frequency and amplitude perturbation measures, long term average spectral measures, and glottal noise measures were extracted from both sustained vowel and continuous speech samples. Our experiments were performed on a published database of 53 normal talkers and 175 talkers with a pathological voice. The classification performance of the nine acoustic measures was quantified using linear discriminant analysis and receiver operating characteristic (ROC) curve analysis. When individual measures were considered in isolation, classification was more accurate for measures extracted from sustained vowels than for those based on continuous speech samples. Classification accuracy improved when combinations of acoustic parameters were considered. For such combinations of measures, classification results were comparable for measures extracted from continuous speech samples and for those based on sustained vowels.

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Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers

Remote Sensing ◽

10.3390/rs11232788 ◽

2019 ◽

Vol 11 (23) ◽

pp. 2788 ◽

Cited By ~ 4

Author(s):

Uwe Knauer ◽

Cornelius Styp von Rekowski ◽

Marianne Stecklina ◽

Tilman Krokotsch ◽

Tuan Pham Minh ◽

...

Keyword(s):

Neural Network ◽

Random Forest ◽

Convolutional Neural Network ◽

Tree Species ◽

Classification Performance ◽

Majority Voting ◽

Imaging Data ◽

Species Classification ◽

Weighted Voting ◽

Linear Discriminant

In this paper, we evaluate different popular voting strategies for fusion of classifier results. A convolutional neural network (CNN) and different variants of random forest (RF) classifiers were trained to discriminate between 15 tree species based on airborne hyperspectral imaging data. The spectral data was preprocessed with a multi-class linear discriminant analysis (MCLDA) as a means to reduce dimensionality and to obtain spatial–spectral features. The best individual classifier was a CNN with a classification accuracy of 0.73 +/− 0.086. The classification performance increased to an accuracy of 0.78 +/− 0.053 by using precision weighted voting for a hybrid ensemble of the CNN and two RF classifiers. This voting strategy clearly outperformed majority voting (0.74), accuracy weighted voting (0.75), and presidential voting (0.75).

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The Effect of Static and Dynamic Visual Stimulations on Error Detection Based on Error-Evoked Brain Responses

Sensors ◽

10.3390/s20164475 ◽

2020 ◽

Vol 20 (16) ◽

pp. 4475

Author(s):

Rui Xu ◽

Yaoyao Wang ◽

Xianle Shi ◽

Ningning Wang ◽

Dong Ming

Keyword(s):

Error Detection ◽

Time Window ◽

Time Windows ◽

Static Condition ◽

Linear Discriminant ◽

Brain Responses ◽

Dynamic Case ◽

Related Potentials ◽

Error Recognition ◽

The Brain

Error-related potentials (ErrPs) have provided technical support for the brain-computer interface. However, different visual stimulations may affect the ErrPs, and furthermore, affect the error recognition based on ErrPs. Therefore, the study aimed to investigate how people respond to different visual stimulations (static and dynamic) and find the best time window for different stimulation. Nineteen participants were recruited in the ErrPs-based tasks with static and dynamic visual stimulations. Five ErrPs were statistically compared, and the classification accuracies were obtained through linear discriminant analysis (LDA) with nine different time windows. The results showed that the P3, N6, and P8 with correctness were significantly different from those with error in both stimulations, while N1 only existed in static. The differences between dynamic and static errors existed in N1 and P2. The highest accuracy was obtained in the time window related to N1, P3, N6, and P8 for the static condition, and in the time window related to P3, N6, and P8 for the dynamic. In conclusion, the early components of ErrPs may be affected by stimulation modes, and the late components are more sensitive to errors. The error recognition with static stimulation requires information from the entire epoch, while the late windows should be focused more within the dynamic case.

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An Optimized Channel Selection Method Based on Multifrequency CSP-Rank for Motor Imagery-Based BCI System

Computational Intelligence and Neuroscience ◽

10.1155/2019/8068357 ◽

2019 ◽

Vol 2019 ◽

pp. 1-10 ◽

Cited By ~ 15

Author(s):

Jian Kui Feng ◽

Jing Jin ◽

Ian Daly ◽

Jiale Zhou ◽

Yugang Niu ◽

...

Keyword(s):

Motor Imagery ◽

Classification Accuracy ◽

Channel Selection ◽

Selection Method ◽

Selection Methods ◽

Eeg Signals ◽

Common Spatial Pattern ◽

Linear Discriminant ◽

Novel Method ◽

Electroencephalogram Eeg

Background. Due to the redundant information contained in multichannel electroencephalogram (EEG) signals, the classification accuracy of brain-computer interface (BCI) systems may deteriorate to a large extent. Channel selection methods can help to remove task-independent electroencephalogram (EEG) signals and hence improve the performance of BCI systems. However, in different frequency bands, brain areas associated with motor imagery are not exactly the same, which will result in the inability of traditional channel selection methods to extract effective EEG features. New Method. To address the above problem, this paper proposes a novel method based on common spatial pattern- (CSP-) rank channel selection for multifrequency band EEG (CSP-R-MF). It combines the multiband signal decomposition filtering and the CSP-rank channel selection methods to select significant channels, and then linear discriminant analysis (LDA) was used to calculate the classification accuracy. Results. The results showed that our proposed CSP-R-MF method could significantly improve the average classification accuracy compared with the CSP-rank channel selection method.

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