Spatial Filter Bank Research Articles

Independent Vector Analysis for Feature Extraction in Motor Imagery Classification.

Independent vector analysis (IVA) can be viewed as an extension of independent component analysis (ICA) to multiple datasets. It exploits the statistical dependency between different datasets through mutual information. In the context of motor imagery classification based on electroencephalogram (EEG) signals for the brain-computer interface (BCI), several methods have been proposed to extract features efficiently, mainly based on common spatial patterns, filter banks, and deep learning. However, most methods use only one dataset at a time, which may not be sufficient for dealing with a multi-source retrieving problem in certain scenarios. From this perspective, this paper proposes an original approach for feature extraction through multiple datasets based on IVA to improve the classification of EEG-based motor imagery movements. The IVA components were used as features to classify imagined movements using consolidated classifiers (support vector machines and K-nearest neighbors) and deep classifiers (EEGNet and EEGInception). The results show an interesting performance concerning the clustering of MI-based BCI patients, and the proposed method reached an average accuracy of 86.7%.

Hybrid Brain-Computer Interface Controlled Soft Robotic Glove for Stroke Rehabilitation.

Soft robotic glove controlled by a brain-computer interface (BCI) have demonstrated effectiveness in hand rehabilitation for stroke patients. Current systems rely on static visual representations for patients to perform motor imagination (MI) tasks, resulting in lower BCI performance. Therefore, this study innovatively used MI and high-frequency steady-state visual evoked potential (SSVEP) to construct a friendly and natural hybrid BCI paradigm. Specifically, the stimulation interface sequentially presented decomposed action pictures of the left and right hands gripping a ball, with the pictures flashing at specific stimulation frequencies (left: 34 Hz, right: 35 Hz). Integrating soft robotic glove as feedback, we established a comprehensive "peripheral - central - peripheral" hand rehabilitation system to facilitate the hand rehabilitation of patients. Filter bank common spatial pattern (FBCSP) and filter bank canonical correlation analysis (FBCCA) algorithms were used to identify MI and SSVEP signals, respectively. Additionally, we proposed a novel fusion algorithm to decide the final output of the system. The feasibility of the proposed system was validated through online experiments involving 12 healthy subjects and 9 stroke patients, achieving accuracy rates of 95.83 ± 6.83% and 63.33 ± 10.38, respectively. The accuracy of MI and SSVEP in 12 healthy subjects reached 81.67 ± 15.63% and 95.14 ± 7.47%, both lower than the accuracy after fusion, these results confirmed the effectiveness of the proposed algorithm. The accuracy rate was more than 50% in both healthy subjects and patients, confirming the effectiveness of the proposed system.

Evaluation of temporal, spatial and spectral filtering in CSP-based methods for decoding pedaling-based motor tasks using EEG signals

Stroke is a neurological syndrome that usually causes a loss of voluntary control of lower/upper body movements, making it difficult for affected individuals to perform Activities of Daily Living (ADLs). Brain-Computer Interfaces (BCIs) combined with robotic systems, such as Motorized Mini Exercise Bikes (MMEB), have enabled the rehabilitation of people with disabilities by decoding their actions and executing a motor task. However, Electroencephalography (EEG)-based BCIs are affected by the presence of physiological and non-physiological artifacts. Thus, movement discrimination using EEG become challenging, even in pedaling tasks, which have not been well explored in the literature. In this study, Common Spatial Patterns (CSP)-based methods were proposed to classify pedaling motor tasks. To address this, Filter Bank Common Spatial Patterns (FBCSP) and Filter Bank Common Spatial-Spectral Patterns (FBCSSP) were implemented with different spatial filtering configurations by varying the time segment with different filter bank combinations for the three methods to decode pedaling tasks. An in-house EEG dataset during pedaling tasks was registered for 8 participants. As results, the best configuration corresponds to a filter bank with two filters (8–19 Hz and 19–30 Hz) using a time window between 1.5 and 2.5 s after the cue and implementing two spatial filters, which provide accuracy of approximately 0.81, False Positive Rates lower than 0.19, and Kappa index of 0.61. This work implies that EEG oscillatory patterns during pedaling can be accurately classified using machine learning. Therefore, our method can be applied in the rehabilitation context, such as MMEB-based BCIs, in the future.

Enhancement of Hybrid BCI System Performance Based on Motor Imagery and SSVEP by Transcranial Alternating Current Stimulation.

The hybrid brain-computer interface (BCI) is verified to reduce disadvantages of conventional BCI systems. Transcranial electrical stimulation (tES) can also improve the performance and applicability of BCI. However, enhancement in BCI performance attained solely from the perspective of users or solely from the angle of BCI system design is limited. In this study, a hybrid BCI system combining MI and SSVEP was proposed. Furthermore, transcranial alternating current stimulation (tACS) was utilized to enhance the performance of the proposed hybrid BCI system. The stimulation interface presented a depiction of grabbing a ball with both of hands, with left-hand and right-hand flickering at frequencies of 34 Hz and 35 Hz. Subjects watched the interface and imagined grabbing a ball with either left hand or right hand to perform SSVEP and MI task. The MI and SSVEP signals were processed separately using filter bank common spatial patterns (FBCSP) and filter bank canonical correlation analysis (FBCCA) algorithms, respectively. A fusion method was proposed to fuse the features extracted from MI and SSVEP. Twenty healthy subjects took part in the online experiment and underwent tACS sequentially. The fusion accuracy post-tACS reached 90.25% ± 11.40%, which was significantly different from pre-tACS. The fusion accuracy also surpassed MI accuracy and SSVEP accuracy respectively. These results indicated the superior performance of the hybrid BCI system and tACS would improve the performance of the hybrid BCI system.

EEG-based investigation of effects of mindfulness meditation training on state and trait by deep learning and traditional machine learning.

This study examines the state and trait effects of short-term mindfulness-based stress reduction (MBSR) training using convolutional neural networks (CNN) based deep learning methods and traditional machine learning methods, including shallow and deep ConvNets as well as support vector machine (SVM) with features extracted from common spatial pattern (CSP) and filter bank CSP (FBCSP). We investigated the electroencephalogram (EEG) measurements of 11 novice MBSR practitioners (6 males, 5 females; mean age 35.7 years; 7 Asians and 4 Caucasians) during resting and meditation at early and late training stages. The classifiers are trained and evaluated using inter-subject, mix-subject, intra-subject, and subject-transfer classification strategies, each according to a specific application scenario. For MBSR state effect recognition, trait effect recognition using meditation EEG, and trait effect recognition using resting EEG, from shallow ConvNet classifier we get mix-subject/intra-subject classification accuracies superior to related previous studies for both novice and expert meditators with a variety of meditation types including yoga, Tibetan, and mindfulness, whereas from FBSCP + SVM classifier we get inter-subject classification accuracies of 68.50, 85.00, and 78.96%, respectively. Deep learning is superior for state effect recognition of novice meditators and slightly inferior but still comparable for both state and trait effects recognition of expert meditators when compared to the literatures. This study supports previous findings that short-term meditation training has EEG-recognizable state and trait effects.

Prefiltering-based reduced rank multiuser detection in uplink generalized spatial modulation massive MIMO system

Exploiting generalized spatial modulation in massive MIMO system, we develop an efficient uplink multiuser detection scheme. Different from the maximum likelihood detection algorithm, the spatial and temporal data in the proposed detection scheme are decoded in a sequential manner: At the front end of base station receiver, a bank of spatial filters is first employed to mitigate the multiuser interference using the benefits of large-scale antenna array. There then, a reduced rank generalized Eigen-value decomposition-based algorithm is proposed to identify the antennas activated at each user terminal. In the second stage, a least-squares-based multiuser detector is employed to decode the multiplexed temporal symbols. Both theoretical analysis and computer simulations are conducted to evaluate system performance in terms of correct identification probability and overall symbol error rate. Simulation results demonstrate that the proposed algorithm outperforms most of the existing algorithms yet the complexity is extensively reduced.

Multi-Class Classification of Upper Limb Movements With Filter Bank Task-Related Component Analysis.

The classification of limb movements can provide with control commands in non-invasive brain-computer interface. Previous studies on the classification of limb movements have focused on the classification of left/right limbs; however, the classification of different types of upper limb movements has often been ignored despite that it provides more active-evoked control commands in the brain-computer interface. Nevertheless, few machine learning method can be used as the state-of-the-art method in the multi-class classification of limb movements. This work focuses on the multi-class classification of upper limb movements and proposes the multi-class filter bank task-related component analysis (mFBTRCA) method, which consists of three steps: spatial filtering, similarity measuring and filter bank selection. The spatial filter, namely the task-related component analysis, is first used to remove noise from EEG signals. The canonical correlation measures the similarity of the spatial-filtered signals and is used for feature extraction. The correlation features are extracted from multiple low-frequency filter banks. The minimum-redundancy maximum-relevance selects the essential features from all the correlation features, and finally, the support vector machine is used to classify the selected features. The proposed method compared against previously used models is evaluated using two datasets. mFBTRCA achieved a classification accuracy of 0.4193 ± 0.0780 (7 classes) and 0.4032 ± 0.0714 (5 classes), respectively, which improves on the best accuracies achieved using the compared methods (0.3590 ± 0.0645 and 0.3159 ± 0.0736, respectively). The proposed method is expected to provide more control commands in the applications of non-invasive brain-computer interfaces.

Comparative analysis of spectral and temporal combinations in CSP-based methods for decoding hand motor imagery tasks

BackgroundA widely used paradigm for brain-computer interfaces (BCI) is based on the detection of event-related (des)synchronization (ERD/S) in response to hand motor imagery (MI) tasks. The common spatial pattern (CSP) has been recognized as a powerful algorithm to design spatial filters for ERD/ERS detection. However, a limitation of CSP focus on identification only of discriminative spatial information but not the spectral one. New methodAn open problem remains in literature related to extracting the most discriminative brain patterns in MI-based BCIs using an optimal time segment and spectral information that accounts for intersubject variability. In recent years, different variants of CSP-based methods have been proposed to address the problem of decoding motor imagery tasks under the intersubject variability of frequency bands related to ERD/ERS events, including Filter Bank Common Spatial Patterns (FBCSP) and Filter Bank Common Spatio-Spectral Patterns (FBCSSP). Comparison with existing methodsWe performed a comparative study of different combinations of time segments and filter banks for three methods (CSP, FBCSP, and FBCSSP) to decode hand (right and left) motor imagery tasks using two different EEG datasets (Gigascience and BCI IVa competition). ResultsThe best configuration corresponds to a filter bank with 3 filters (8–15 Hz, 15–22 Hz and 22–29 Hz) using a time window of 1.5 s after the trigger, which provide accuracies of approximately 74% and an estimated ITRs of approximately 7 bits/min. ConclusionDiscriminative information in time and spectral domains could be obtained using a convenient filter bank and a time segment configuration, to enhance the classification rate and ITR for detection of hand motor imagery tasks with CSP-related methods, to be used in the implementation of a real-time BCI system.

The Minimum Overlap-Gap Algorithm for Speech Enhancement

In this paper, we propose a novel speech enhancement paradigm which can effectively solve the problem of retrieving a desired speech signal in a multi-talker environment. The proposed speech enhancement paradigm involves a three-step procedure consisting of separation, ranking, and enhancement. First, a speech separation system – which could be a conventional spatial filter bank or more advanced separation systems – separates mixtures of speech signals captured by microphones into speech signals from candidate speakers. Next, novel ranking algorithms – proposed in this paper – are applied to determine the talker-of-interest amongst the separated speech signals. Finally, the speech signal of the talker-of-interest is estimated as a linear combination of the separated signals, whose weights are determined by the ranking algorithms. We propose ranking algorithms, which exploit turn-taking patterns between conversational partners in order to determine the talker-of-interest amongst competing speakers. Unlike some existing solutions, our ranking algorithms do not require access to additional sensors, e.g., EEG electrodes, cameras, etc., but only rely on microphone signals. Specifically, the proposed algorithms rank the separated speech signals based on the probability of speech overlaps and gaps with the user’s own voice. The speech signal with highest ranking is the talker with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">minimum</i> probability of speech overlap and gap with the user’s own voice. The proposed ranking algorithms are shown highly effective at determining the talker-of-interest, since conversational partners, i.e., the user and the talker-of-interest, behaviorally avoid speech overlaps and gaps. We evaluate the proposed speech enhancement paradigm in two practical hearing aid related applications, where the objective is to enhance a speech signal of a conversational partner in a multi-talker environment. The results of the evaluation demonstrate that the proposed speech enhancement systems in both applications significantly outperform conventional speech enhancement systems.

Visual perception of texture regularity: Conjoint measurements and a wavelet response-distribution model.

Texture regularity, such as the repeating pattern in a carpet, brickwork or tree bark, is a ubiquitous feature of the visual world. The perception of regularity has generally been studied using multi-element textures in which the degree of regularity has been manipulated by adding random jitter to the elements' positions. Here we used three-factor Maximum Likelihood Conjoint Measurement (MLCM) for the first time to investigate the encoding of regularity information under more complex conditions in which element spacing and size, in addition to positional jitter, were manipulated. Human observers were presented with large numbers of pairs of multi-element stimuli with varying levels of the three factors, and indicated on each trial which stimulus appeared more regular. All three factors contributed to regularity perception. Jitter, as expected, strongly affected regularity perception. This effect of jitter on regularity perception is strongest at small element spacing and large texture element size, suggesting that the visual system utilizes the edge-to-edge distance between elements as the basis for regularity judgments. We then examined how the responses of a bank of Gabor wavelet spatial filters might account for our results. Our analysis indicates that the peakedness of the spatial frequency (SF) distribution, a previously favored proposal, is insufficient for regularity encoding since it varied more with element spacing and size than with jitter. Instead, our results support the idea that the visual system may extract texture regularity information from the moments of the SF-distribution across orientation. In our best-performing model, the variance of SF-distribution skew across orientations can explain 70% of the variance of estimated texture regularity from our data, suggesting that it could provide a candidate read-out for perceived regularity.

Distinguishable spatial-spectral feature learning neural network framework for motor imagery-based brain–computer interface

Objective. Spatial and spectral features extracted from electroencephalogram (EEG) are critical for the classification of motor imagery (MI) tasks. As prevalently used methods, the common spatial pattern (CSP) and filter bank CSP (FBCSP) can effectively extract spatial-spectral features from MI-related EEG. To further improve the separability of the CSP features, we proposed a distinguishable spatial-spectral feature learning neural network (DSSFLNN) framework for MI-based brain–computer interfaces (BCIs) in this study. Approach. The first step of the DSSFLNN framework was to extract FBCSP features from raw EEG signals. Then two squeeze-and-excitation modules were used to re-calibrate CSP features along the band-wise axis and the class-wise axis, respectively. Next, we used a parallel convolutional neural network module to learn distinguishable spatial-spectral features. Finally, the distinguishable spatial-spectral features were fed to a fully connected layer for classification. To verify the effectiveness of the proposed framework, we compared it with the state-of-the-art methods on BCI competition IV datasets 2a and 2b. Main results. The results showed that the DSSFLNN framework can achieve a mean Cohen’s kappa value of 0.7 on two datasets, which outperformed the state-of-the-art methods. Moreover, two additional experiments were conducted and they proved that the combination of band-wise feature learning and class-wise feature learning can achieve significantly better performance than only using either one of them. Significance. The proposed DSSFLNN can effectively improve the decoding performance of MI-based BCIs.

Sub-band target alignment common spatial pattern in brain-computer interface

Background and objectiveIn the brain computer interface (BCI) field, using sub-band common spatial pattern (SBCSP) and filter bank common spatial pattern (FBCSP) can improve the accuracy of classification by selection a specific frequency band. However, in the cross-subject classification, due to the individual differences between different subjects, the performance is limited. MethodsThis paper introduces the idea of transfer learning and presents the sub-band target alignment common spatial pattern (SBTACSP) method and applies it to the cross-subject classification of motor imagery (MI) EEG signals. First, the EEG signals are bandpass-filtered into multiple frequency bands (sub-band filtering). Subsequently, the source domain trails are aligned into the target domain space in each frequency band. The CSP algorithm is then employed to extract features among which more representative features are selected by the minimum redundancy maximum relevance (mRMR) approach from each sub-band. Then the features of all sub-bands are fused. Finally, conventional linear discriminant analysis (LDA) algorithm is used for MI classification. ResultsOur method is evaluated on Datasets Ⅱa and Ⅱb of the BCI Competition Ⅳ. Compared with six state-of-the-art algorithms, the proposed SBTACSP method performed relatively the best and achieved a mean classification accuracy of 75.15% and 66.85% in cross-subject classification of Datasets Ⅱa and Ⅱb respectively. ConclusionTherefore, the combination of sub-band filtering and transfer learning achieves superior classification performance compared to either one. The proposed algorithms will greatly promote the practical application of MI based BCIs.

Deep Temporal-Spatial Feature Learning for Motor Imagery-Based Brain–Computer Interfaces

Motor imagery (MI) decoding is an important part of brain-computer interface (BCI) research, which translates the subject's intentions into commands that external devices can execute. The traditional methods for discriminative feature extraction, such as common spatial pattern (CSP) and filter bank common spatial pattern (FBCSP), have only focused on the energy features of the electroencephalography (EEG) and thus ignored the further exploration of temporal information. However, the temporal information of spatially filtered EEG may be critical to the performance improvement of MI decoding. In this paper, we proposed a deep learning approach termed filter-bank spatial filtering and temporal-spatial convolutional neural network (FBSF-TSCNN) for MI decoding, where the FBSF block transforms the raw EEG signals into an appropriate intermediate EEG presentation, and then the TSCNN block decodes the intermediate EEG signals. Moreover, a novel stage-wise training strategy is proposed to mitigate the difficult optimization problem of the TSCNN block in the case of insufficient training samples. Firstly, the feature extraction layers are trained by optimization of the triplet loss. Then, the classification layers are trained by optimization of the cross-entropy loss. Finally, the entire network (TSCNN) is fine-tuned by the back-propagation (BP) algorithm. Experimental evaluations on the BCI IV 2a and SMR-BCI datasets reveal that the proposed stage-wise training strategy yields significant performance improvement compared with the conventional end-to-end training strategy, and the proposed approach is comparable with the state-of-the-art method.

Efficient Beamforming Technique Based on Sparse MIMO Array and Spatial Filter Bank

Spatial diversity gained through different beamforming techniques in antenna array is one of the main advantages over the single antenna. Antenna complexity and beamwidth broadening at the scanning angles other than broadside are downsides of antenna arrays that prevent its practical implementation from some applications. In this letter, we propose an efficient beamforming method based on the sparse multiple-input–multiple-output (MIMO) array and spatial filter bank (SFB). The sparse MIMO array produces narrow beams while significantly reduces the number of array elements without compromising system performance. Multiple main beams are chosen from grating lobes by SFB. By combining sparse array geometry and SFB, we can achieve a narrower beamwidth with less antenna elements, multiple beams, and constant beamwidth. The burden of system will be transferred from the frontend hardware to the signal processing back end.

Boosting the information transfer rate of an SSVEP-BCI system using maximal-phase-locking value and minimal-distance spatial filter banks

For Brain-Computer Interface (BCI) systems, improving the Information Transfer Rate (ITR) is a very critical issue. This study focuses on a Steady-State Visually Evoked Potential (SSVEP)-based BCI because of its advantage of high ITR. Unsupervised Canonical Correlation Analysis (CCA)-based method has been widely employed because of its high efficiency and easy implementation. In a recent study, an ensemble-CCA method based on individual training data was proposed and achieved an excellent performance with ITR of 267 bit/min. A 40-target SSVEP-BCI speller was investigated in this study, using an integration of Minimal-Distance (MD) and Maximal-Phase-locking value (MP) approaches. In the MD approach, a spatial filter is developed to minimize the distance between the training data and the reference sine signal, and in this study, two different types of distance were compared. In the MP approach, a spatial filter is developed to maximize the Phase-Locking Value (PLV) between the training calibration data and the reference sine signal. In addition to the fundamental frequency of stimulation, the harmonics were used to train MD and MP spatial filters, which formed spatial filter banks. The test data epoch was multiplied by the MP and MD spatial filter banks, and the distances and PLVs were extracted as features for recognition. Across 12 subjects with a 0.4 s-data length, the proposed method realized an average classification accuracy and ITR of 93% and 307 bit/min, respectively, which is significantly higher than the current state-of-the-art method. To the best of our knowledge, these results suggest that the proposed method has achieved the highest ITR in SSVEP-BCI studies.

A Computational Model for the Neural Representation and Estimation of the Binocular Vector Disparity from Convergent Stereo Image Pairs.

The depth cue is a fundamental piece of information for artificial and living beings who interact with the surrounding environment in order to handle objects and to avoid obstacles: in such situations, the disparity patterns, which arise when agents fixate objects, are vector fields. We propose a biologically-inspired computational model to estimate dense horizontal and vertical disparity maps by exploiting the cortical paradigms of the primate visual system: in particular, we aim to model the disparity sensitivity of the V1-MT visual pathway. The proposed model is based on a first processing stage composed of a bank of spatial band-pass filters and a static nonlinearity, mimicking complex binocular cells. Then, subsequent pooling stages and decoding strategies allow the model to estimate the vector disparity, after having represented it as a population of MT-like units. We assess the proposed model by using standard benchmarking stereo images, the Middlebury dataset, and specific stereo images that have horizontal and vertical disparities, which characterize the stimuli produced by active vision systems. Moreover, we systemically analyze how the different processing stages affect the model performance, and we discuss their implications for the neural modeling.

Design and implementation of an eye gesture perception system based on electrooculography

People with motor diseases have suffered from deprivation of both verbal and non-verbal communication abilities. Fortunately, some of them still retain coordination of brain and eye-motor. To establish a stable communication way for these disabled people, this paper presents an eye gesture perception system based on Electrooculography (EOG). In order to implement a high-accuracy of unit saccadic EOG signals recognition, we propose a new feature extraction algorithm based on Common Spatial Pattern (CSP). We first establish a CSP spatial filter bank corresponding to 8 saccadic tasks (i.e., up, down, left, right, right-up, left-up, right-down, and left-down), then use it to linearly project raw EOG signals and treat the outputs as feature parameters. Furthermore, eye gestures recognition has been carried out by identifying and merging unit saccadic segments in terms of pre-defined time sequences. Experiential results over 10 subjects show that the recognition precision of unit saccadic EOG and eye gesture are 96.8% and 95.0% respectively, which reveal the proposed system has a good performance of eye gestures perception.

Invariant texture classification using a spatial filter bank in multi-resolution analysis

This paper proposed a new method based on spatial filter banks and discrete wavelet transform (DWT) for invariant texture classification. The method used a multi-resolution analysis method like DWT and applied the proposed filter bank on different resolutions. Then, a simple fusion of features on different resolutions was used for invariant texture analysis. A comprehensive study was done to examine the effectiveness of the proposed method. Different datasets with different properties were used in this paper such as Brodatz, Outex, and KTH-TIPS for the evaluation. Local binary pattern (LBP) methods have been one of the powerful methods in recent years for invariant texture classification. A comparative study was performed with some state-of-the-art LBP methods. This comparison indicated promising results for the proposed approach as compared with the LBP methods.

A spatial filter bank model of the Veselago–Pendry superlens

We present a canonical filter bank model that emulates the transmission of both propagating and evanescent electromagnetic fields through a Veselago–Pendry Superlens. The model consists of an array of coupled resonator pairs; each pair transmits one term of the spatial Fourier series of the object field. In addition to emulating the steady-state transfer function of the superlens, the model also approximates its dynamic response. Closed-form expressions for the values of the circuit elements are derived in terms of the geometry and the constitutive parameters of the lens at the frequency of operation in the steady state. Losses can be included by adding dissipative elements to the equivalent circuit. The concept of the Veselago–Pendry superlens as a filter in k-space provides physical insight into the resonant nature of image transmission and may suggest new ways to realize such a device.

Blind multipath separation and combining technique for signal recovery

To achieve better mitigation of both cochannel interference (CCI) and intersymbol interference, a new structure using generalized estimation of multipath signals in conjunction with maximal-ratio combining diversity for wireless communications over multipath channels is introduced. In this structure, the signal replicas received from multiple paths are first independently produced by a bank of blind spatial filters and then constructively combined by a diversity combining receiver for final signal estimate. The new scheme can be applied on single antenna array or between multiple antenna subarrays. It will be shown, from both theoretical analysis and numerical experiments, that the new scheme provides both space diversity gains and path diversity gains while suppressing the CCIs.