Subject-specific Frequency Bands Research Articles

SincMSNet: a Sinc filter convolutional neural network for EEG motor imagery classification.

Objective.Motor imagery (MI) is widely used in brain-computer interfaces (BCIs). However, the decode of MI-EEG using convolutional neural networks (CNNs) remains a challenge due to individual variability.Approach.We propose a fully end-to-end CNN called SincMSNet to address this issue. SincMSNet employs the Sinc filter to extract subject-specific frequency band information and utilizes mixed-depth convolution to extract multi-scale temporal information for each band. It then applies a spatial convolutional block to extract spatial features and uses a temporal log-variance block to obtain classification features. The model of SincMSNet is trained under the joint supervision of cross-entropy and center loss to achieve inter-class separable and intra-class compact representations of EEG signals.Main results.We evaluated the performance of SincMSNet on the BCIC-IV-2a (four-class) and OpenBMI (two-class) datasets. SincMSNet achieves impressive results, surpassing benchmark methods. In four-class and two-class inter-session analysis, it achieves average accuracies of 80.70% and 71.50% respectively. In four-class and two-class single-session analysis, it achieves average accuracies of 84.69% and 76.99% respectively. Additionally, visualizations of the learned band-pass filter bands by Sinc filters demonstrate the network's ability to extract subject-specific frequency band information from EEG.Significance.This study highlights the potential of SincMSNet in improving the performance of MI-EEG decoding and designing more robust MI-BCIs. The source code for SincMSNet can be found at:https://github.com/Want2Vanish/SincMSNet.

Optimize temporal configuration for motor imagery-based multiclass performance and its relationship with subject-specific frequency

Enhancing the performance of motor imagery-based Brain-Computer Interfaces (BCI) has been a significant goal in the BCI field. To achieve such a goal, several typical and promising techniques have been implemented, such as developing intelligent algorithms, combining features from different domains, extracting subject-specific parameters, and so forth. Previous studies performing temporal segmentation often ended up with a large number of features and placed a burden on computational cost, which poses a disadvantage to online analysis. This study proposes a novel approach to utilizing short-window segments to find an optimal combination of time segments and feature extractors. Electroencephalogram data from four datasets (BCI Competition IV dataset 2a, 2b and two self-acquired datasets) were segmented into four types of the time window and had features extracted by Common Spatial Pattern and its variants, and lastly classified by Linear Discriminant Analysis. The result shows that the combination of the “2-s with 1-s overlapping” segment and Filter Bank Common Spatial Pattern yields overall accuracy of 2–6.5% (p-value <0.05), higher than other methods in comparison. In addition, the study finds that there is a negative correlation (r = −0.38) between the number of subject-specific frequency bands and the performance (p-value <0.0001). The results demonstrate that the narrower and more focus frequency range chosen, the more accurate the model can achieve. Our results indicate that the “2-s with 1-s overlapping” segment would be an ideal candidate for online BCI analysis, and the response of selected frequency bands could be an informative indicator to evaluate BCI performance.

Time-Frequency Analysis of Scalp EEG With Hilbert-Huang Transform and Deep Learning.

Electroencephalography (EEG) is a brain imaging approach that has been widely used in neuroscience and clinical settings. The conventional EEG analyses usually require pre-defined frequency bands when characterizing neural oscillations and extracting features for classifying EEG signals. However, neural responses are naturally heterogeneous by showing variations in frequency bands of brainwaves and peak frequencies of oscillatory modes across individuals. Fail to account for such variations might result in information loss and classifiers with low accuracy but high variation across individuals. To address these issues, we present a systematic time-frequency analysis approach for analyzing scalp EEG signals. In particular, we propose a data-driven method to compute the subject-specific frequency bands for brain oscillations via Hilbert-Huang Transform, lifting the restriction of using fixed frequency bands for all subjects. Then, we propose two novel metrics to quantify the power and frequency aspects of brainwaves represented by sub-signals decomposed from the EEG signals. The effectiveness of the proposed metrics are tested on two scalp EEG datasets and compared with four commonly used features sets extracted from wavelet and Hilbert-Huang Transform. The validation results show that the proposed metrics are more discriminatory than other features leading to accuracies in the range of 94.93% to 99.84%. Besides classification, the proposed metrics show great potential in quantification of neural oscillations and serving as biomarkers in the neuroscience research.

Multiple graph fusion based on Riemannian geometry for motor imagery classification

In motor imagery-based brain-computer interfaces (BCIs), the spatial covariance features of electroencephalography (EEG) signals that lie on Riemannian manifolds are used to enhance the classification performance of motor imagery BCIs. However, the problem of subject-specific bandpass frequency selection frequently arises in Riemannian manifold-based methods. In this study, we propose a multiple Riemannian graph fusion (MRGF) model to optimize the subject-specific frequency band for a Riemannian manifold. After constructing multiple Riemannian graphs corresponding to multiple bandpass frequency bands, graph embedding based on bilinear mapping and graph fusion based on mutual information were applied to simultaneously extract the spatial and spectral features of the EEG signals from Riemannian graphs. Furthermore, with a support vector machine (SVM) classifier performed on learned features, we obtained an efficient algorithm, which achieves higher classification performance on various datasets, such as BCI competition IIa and in-house BCI datasets. The proposed methods can also be used in other classification problems with sample data in the form of covariance matrices.

Motor imagery EEG signal classification using upper triangle filter bank auto-encode method

In motor-imagery-based brain–computer interfaces, the frequency, and spatial information of electroencephalography signals can be used to improve the performance of motor imagery classification. However, the problem of subject-specific frequency band selection occurs frequently in spatial feature extraction. In this study, to enhance the frequency information in a spatial filter, we design an upper triangle filter bank to determine discriminative frequency components and apply the common spatial pattern to extract spatial features from sub-bands. Furthermore, an autoencoder neural network is constructed to reduce the high dimensionality of spatial features. The classification performance of the proposed method is experimentally evaluated on motor imagery datasets. The proposed method provides more discriminative features and higher classification performance in comparison with competing algorithms. This proposed filter bank method can be used to extend the other spatial and spectral processing method for motor imagery classification.

Combined Optimization of Frequency Band and Time Segment Using Quantum Particle Swarm Algorithm for Brain-Computer Interfaces

Common spatial pattern (CSP) is a popular algorithm for spatial filtering and subsequent feature extraction in motor imagery-based (MI) brain-computer interface (BCI) systems. The performance of CSP, however, depends heavily on the subject-specific frequency band and time segment used for classifying mental tasks. Accurate selection of most informative frequency band and time segment poses a great challenge. In this study, quantum particle swarm optimization is proposed for sole selection of frequency band and joint selection of frequency band and time segment, which are realized by a wrapping approach, incorporating CSP for feature extraction and support vector machine for classification into the classification model. The classification error rate is used as the fitness function of quantum particle swarm optimization. The classification performance of quantum particle swarm optimization based CSP algorithm for joint selection of frequency band and time segment is evaluated by comparing with other three CSP algorithms using either fixed frequency band and time segment or fixed time segment and the frequency band selected by particle swarm optimization and quantum-behaved particle swarm optimization, on two MI data sets with different number of channels and trials. Experimental results suggest that the proposed algorithm outperforms the other three algorithms in terms of classification error rate. Across all subjects from the two data sets, the averaged error rate of the proposed algorithm was 7.45%, 2.97% and 2.05% lower than the CSP with fixed frequency band and time segment, that with selected frequency bands by particle swarm optimization and that with selected bands by quantum particle swarm optimization. The proposed algorithm can facilitate the real-world application of BCIs.

A wrapped time-frequency combined selection in the source domain

The selection of time segment and frequency band always play a vital role in the decoding of Motor Imagery Tasks (MI-tasks), especially for the feature extraction of MI-Electroencephalographic (MI-EEG). The excavation of valuable and discriminative feature information needs to be based on the reliable time-frequency analysis, which is the foremost precondition for feature engineering. However, relying on the high temporal resolution of MI-EEG, traditional feature extraction methods can only conduct the time-frequency analysis according to the superficial neurophysiological rhythm of EEG in the sensor domain. And more detailed time-frequency characteristics could hardly be embodied in a few channels of MI-EEG signals, which leads to a coarse selection of time-frequency interval and the resulted lower decoding effect. Therefore, a neurophysiology-based technique is needed for performing more exact time-frequency analysis. Based on the advanced EEG Source Imaging, a Wrapped Time-Frequency combined Selection in the Source Domain, which is denoted as WTFS-SD, is proposed for decoding the MI-tasks by applying Weighted Minimum Norm Estimate and CSP based sub-band feature extraction in this paper. Abundant comparative experiments are conducted on the BCI2000 system dataset with six subjects, and the results show that the proposed methods can select subject-specific optimal frequency band and TOI, which yields the highest average classification rate of 93.14% by 9-fold cross-validation at the same chance level as well as a superior mean kappa coefficient of 0.8627 across all subjects compared to other prevalent methods. This study will enhance the decoding of complex MI-tasks and be helpful for the development of intelligent BCI system.

Classification of Motor Imagery Using a Combination of User-Specific Band and Subject-Specific Band for Brain-Computer Interface

The essential task of a Brain-Computer Interface (BCI) is to extract the motor imagery features from Electro-Encephalogram (EEG) signals for classifying the thought process. It is necessary to analyse these obtained signals in both the time domain and frequency domains. It is observed that the combination of multiple algorithms increases the performance of the feature extraction process. This paper identifies combinations that have not been attempted previously and improves the accuracy of the overall process, although other authors implemented different combinations of the techniques. The focus is given more on the feature extraction process and frequency bands, which are user-specific and subject-specific frequency bands. In both time and frequency domains, after analysing EEG signals with the time domain parameter, we select the frequency band and the timing while using the Fisher ratio of the time domain parameter (TDP). We used Fisher discriminant analysis (FDA)-type F-score to simultaneously select the frequency band and time segment for multi-class classification. We extracted subject-specific TDP features from the training trials to train the classifier when optimal time-frequency areas were selected for each subject. In this paper, various methods are explored for obtaining the features, which are Time Domain Parameters (TDP), Fast Fourier Transform (FFT), Principal Component Analysis (PCA), R2, Fast Correlation Based Filter (FCBF), Empirical Mode Decomposition (EMD), and Intrinsic time-scale decomposition (ITD). After the extraction process, PCA is used for dimensionality reduction. An efficient result was obtained with the combination of TDP, FFT, and PCA. We used the multi-class Fisher’s linear discriminant analysis (LDA) as the classifier, which was in line with the FDA-type F-score. It is observed that the combination of feature extraction techniques to the frequency bands that were selected by the Fisher ratio and FDA type F-score along with Fisher’s LDA classifier had higher accuracy than the results obtained other researches. A kappa coefficient accuracy of 0.64 is obtained for the proposed technique. Our method leads to better classification performance when compared to state-of-the-art methods. The novelty of the approach is based on the combination of frequency bands and two feature extraction methods.

Neurophysiological predictors and spectro-spatial discriminative features for enhancing SMR-BCI

Neural engineering research is actively engaged in optimizing the robustness of sensorimotor rhythms (SMR)—brain–computer interface (BCI) to boost its potential real-world use. Objective. This paper investigates two vital factors in efficient and robust SMR-BCI design—algorithms that address subject-variability of optimal features and neurophysiological factors that correlate with BCI performance. Existing SMR-BCI research using electroencephalogram (EEG) to classify bilateral motor imagery (MI) focus on identifying subject-specific frequency bands with most discriminative motor patterns localized to sensorimotor region. Approach. A novel strategy to further optimize BCI performance by taking into account the variability of discriminative spectral regions across various EEG channels is proposed in this paper. Main results. The proposed technique results in a significant () increase in average () classification accuracy by accompanied by a considerable reduction in number of channels and bands. The session-to-session transfer variation in spectro-spatial patterns using proposed algorithm is investigated offline and classification performance of the optimized BCI model is successfully evaluated in an online SMR-BCI. Further, the effective prediction of SMR-BCI performance with physiological indicators derived from multi-channel resting-state EEG is demonstrated. The results indicate that the resting state activation patterns such as entropy and gamma power from pre-motor (fronto-central) and posterior (parietal and centro-parietal) areas, and beta power from posterior (centro-parietal) areas estimate BCI performance with minimum error. These patterns, strongly related to BCI performance, may represent certain cognitive states during rest. Significance. Findings reported in this paper imply the need for subject-specific modelling of BCI and the prediction of BCI performance using multi-channel rest-state parameters, to ensure enhanced BCI performance.

A Randomised Ensemble Learning Approach for Multiclass Motor Imagery Classification Using Error Correcting Output Coding.

Common Spectral Pattern (CSP) algorithm remains predominant for feature extraction from multichannel EEG motor imagery data. However, multiclass classification of from this featureset has been a challenging job. Different approaches have been proposed to be applied on featureset of different EEG subbands to achieve significant classification accuracy. Ensemble learning is very effective in this context to achieve higher accuracy in Brain-Computer Interface (BCI) domain. In this study, we have proposed enhanced classification algorithms to achieve higher classification accuracies. The methods were evaluated against the motor imagery data from Dataset 2a of the publicly available BCI Competition IV (2008). This dataset consists of 22 channels EEG data of 9 subjects for four different movements. A tree based ensemble approach for supervised classification, Extra-Trees algorithm, has been proposed in this paper and also evaluated for its efficacy on this dataset to classify between left hand and right hand movement imaginations. Moreover, this classifier has its inherent capability to select optimum features. Furthermore, in this paper an extension of the binary classification into multiclass domain is also implemented with error correcting output codes (ECOC) approach using the same dataset. Subject-specific frequency bands $\alpha$ (8-12Hz) and $\beta$ (12-30Hz) along with $HG$ (70-100Hz) were considered to extract CSP features. We have achieved an individual peak accuracy of 98ȥ and 84ȥ in binary class and multiclass classification respectively. Furthermore, the results yielded a mean kappa value of 0.58 across all the subjects. This kappa value is higher than of the winner of competition and also from the most of the other approaches applied in this dataset.

A Deep Convolutional Neural Network Based Classification Of Multi-Class Motor Imagery With Improved Generalization.

Motor imagery (MI) based brain-computer interface (BCI) plays a crucial role in various scenarios ranging from post-traumatic rehabilitation to control prosthetics. Computer-aided interpretation of MI has augmented prior mentioned scenarios since decades but failed to address interpersonal variability. Such variability further escalates in case of multi-class MI, which is currently a common practice. The failures due to interpersonal variability can be attributed to handcrafted features as they failed to extract more generalized features. The proposed approach employs convolution neural network (CNN) based model with both filtering (through axis shuffling) and feature extraction to avail end-to-end training. Axis shuffling is performed adopted in initial blocks of the model for 1D preprocessing and reduce the parameters required. Such practice has avoided the overfitting which resulted in an improved generalized model. Publicly available BCI Competition-IV 2a dataset is considered to evaluate the proposed model. The proposed model has demonstrated the capability to identify subject-specific frequency band with an average and highest accuracy of 70.5% and S3.6% respectively. Proposed CNN model can classify in real time without relying on accelerated computing device like GPU.

Discriminative spatial-frequency-temporal feature extraction and classification of motor imagery EEG: An sparse regression and Weighted Naïve Bayesian Classifier-based approach

BackgroundCommon spatial pattern (CSP) is most widely used in motor imagery based brain-computer interface (BCI) systems. In conventional CSP algorithm, pairs of the eigenvectors corresponding to both extreme eigenvalues are selected to construct the optimal spatial filter. In addition, an appropriate selection of subject-specific time segments and frequency bands plays an important role in its successful application. New methodThis study proposes to optimize spatial-frequency-temporal patterns for discriminative feature extraction. Spatial optimization is implemented by channel selection and finding discriminative spatial filters adaptively on each time-frequency segment. A novel Discernibility of Feature Sets (DFS) criteria is designed for spatial filter optimization. Besides, discriminative features located in multiple time-frequency segments are selected automatically by the proposed sparse time-frequency segment common spatial pattern (STFSCSP) method which exploits sparse regression for significant features selection. Finally, a weight determined by the sparse coefficient is assigned for each selected CSP feature and we propose a Weighted Naïve Bayesian Classifier (WNBC) for classification. ResultsExperimental results on two public EEG datasets demonstrate that optimizing spatial-frequency-temporal patterns in a data-driven manner for discriminative feature extraction greatly improves the classification performance. Comparison with existing methodsThe proposed method gives significantly better classification accuracies in comparison with several competing methods in the literature. ConclusionsThe proposed approach is a promising candidate for future BCI systems.

A closed-loop BCI system for rehabilitation of the hemiplegic upper-limb: A performance study of the systems ability to detect intention of movement

Rehabilitation of the upper-limb by means of a brain computer interface (BCI) is based on detecting an “intention to act” in the brain and transforming this intention into actual movement by using a motorized orthesis. The visual and proprioceptive feedback that results from the systems’ generated movement following the subjects’ captured intention, is thought to close the sensori-motor loop. Closing the loop is a key feature of BCI-systems that is thought to induce cerebral plasticity and thereby facilitating rehabilitation. Ten chronic patients post-stroke with a severe upper-limb motor deficit and 10 healthy controls were included in the study. To mobilize wrist and finger movement, we used a pneumatic orthesis that was controlled by the BCI during a wrist flexion/extension task. The main objective was to evaluate the systems’ CPS capacity to “classify” the observed brain activation as either movement or rest. Activation was registered with a 27-channel g tec EEG during two series of 40 randomized trials (20 rest, 20 movements). The sample frequency was 256 Hz. The BCI classifier was capable to distinguish between movements and rest with comparable performance for patients and controls and equal levels of true positives and negatives (78% vs 77% for patients and 81% vs 80% for controls). The CSP classifier used a wide 8–30 Hz-frequency band filter to evaluate EEG activity. By cutting this large band into smaller sub-units, we showed that the systems’ performance could be optimized by using subject-specific smaller frequency bands. The pneumatic orthesis should be improved. The present closed-loop BCI-system for rehabilitation of the paretic upper-limb was well able to recognize intention of movement in the brain. Its practical interest for rehabilitation post-stroke needs to be confirmed by a longitudinal efficiency study.

Improving the discrimination of hand motor imagery via virtual reality based visual guidance

While research on the brain–computer interface (BCI) has been active in recent years, how to get high-quality electrical brain signals to accurately recognize human intentions for reliable communication and interaction is still a challenging task. The evidence has shown that visually guided motor imagery (MI) can modulate sensorimotor electroencephalographic (EEG) rhythms in humans, but how to design and implement efficient visual guidance during MI in order to produce better event-related desynchronization (ERD) patterns is still unclear. The aim of this paper is to investigate the effect of using object-oriented movements in a virtual environment as visual guidance on the modulation of sensorimotor EEG rhythms generated by hand MI. To improve the classification accuracy on MI, we further propose an algorithm to automatically extract subject-specific optimal frequency and time bands for the discrimination of ERD patterns produced by left and right hand MI. The experimental results show that the average classification accuracy of object-directed scenarios is much better than that of non-object-directed scenarios (76.87% vs. 69.66%). The result of the t-test measuring the difference between them is statistically significant (p = 0.0207). When compared to algorithms based on fixed frequency and time bands, contralateral dominant ERD patterns can be enhanced by using the subject-specific optimal frequency and the time bands obtained by our proposed algorithm. These findings have the potential to improve the efficacy and robustness of MI-based BCI applications.

Mutual information-based optimization of sparse spatio-spectral filters in brain–computer interface

Recently, neuro-rehabilitation based on brain---computer interface (BCI) has been considered one of the important applications for BCI. A key challenge in this system is the accurate and reliable detection of motor imagery. In motor imagery-based BCIs, the common spatial patterns (CSP) algorithm is widely used to extract discriminative patterns from electroencephalography signals. However, the CSP algorithm is sensitive to noise and artifacts, and its performance depends on the operational frequency band. To address these issues, this paper proposes a novel optimized sparse spatio-spectral filtering (OSSSF) algorithm. The proposed OSSSF algorithm combines a filter bank framework with sparse CSP filters to automatically select subject-specific discriminative frequency bands as well as to robustify against noise and artifacts. The proposed algorithm directly selects the optimal regularization parameters using a novel mutual information-based approach, instead of the cross-validation approach that is computationally intractable in a filter bank framework. The performance of the proposed OSSSF algorithm is evaluated on a dataset from 11 stroke patients performing neuro-rehabilitation, as well as on the publicly available BCI competition III dataset IVa. The results show that the proposed OSSSF algorithm outperforms the existing algorithms based on CSP, stationary CSP, sparse CSP and filter bank CSP in terms of the classification accuracy, and substantially reduce the computational time of selecting the regularization parameters compared with the cross-validation approach.

Optimizing spatial spectral patterns jointly with channel configuration for brain–computer interface

The power of common spatial pattern (CSP) has been widely validated in electroencephalogram (EEG) based brain–computer interface (BCI). However, its effectiveness is highly dependent on subject-specific time segment, channel configuration and frequency band. Hence, the preprocessing procedure of CSP algorithm is critical to enhance the performance of BCI system. This paper proposes a feature extraction and selection method based on common spatial and spectral pattern for motor imagery brain–computer interface (BCI). We formulate the optimization of spatial spectral patterns, channel configuration and time segment as maximizing the proposed criterions including mutual information algorithm, Fisher ratio algorithm and wrapper method. The proposed method is evaluated on single trial EEG from dataset IVa of BCI competition III. The results show that best features are selected by a wrapper method and these features in cross-validation yield better performance compared to most of the reported results.

Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b

The Common Spatial Pattern (CSP) algorithm is an effective and popular method for classifying 2-class motor imagery electroencephalogram (EEG) data, but its effectiveness depends on the subject-specific frequency band. This paper presents the Filter Bank Common Spatial Pattern (FBCSP) algorithm to optimize the subject-specific frequency band for CSP on Datasets 2a and 2b of the Brain-Computer Interface (BCI) Competition IV. Dataset 2a comprised 4 classes of 22 channels EEG data from 9 subjects, and Dataset 2b comprised 2 classes of 3 bipolar channels EEG data from 9 subjects. Multi-class extensions to FBCSP are also presented to handle the 4-class EEG data in Dataset 2a, namely, Divide-and-Conquer (DC), Pair-Wise (PW), and One-Versus-Rest (OVR) approaches. Two feature selection algorithms are also presented to select discriminative CSP features on Dataset 2b, namely, the Mutual Information-based Best Individual Feature (MIBIF) algorithm, and the Mutual Information-based Rough Set Reduction (MIRSR) algorithm. The single-trial classification accuracies were presented using 10 × 10-fold cross-validations on the training data and session-to-session transfer on the evaluation data from both datasets. Disclosure of the test data labels after the BCI Competition IV showed that the FBCSP algorithm performed relatively the best among the other submitted algorithms and yielded a mean kappa value of 0.569 and 0.600 across all subjects in Datasets 2a and 2b respectively.

Subject and class specific frequency bands selection for multiclass motor imagery classification

AbstractEEG‐based discrimination among motor imagery states has been widely studied for brain‐computer interfaces (BCIs) due to the great potential for real‐life applications. However, in terms of designing a motor imagery‐based BCI system, a lot of research in the literature either uses a frequency band of interest selected manually based on the visual analysis of EEG data or is set to a general broad band, causing performance degradation in classification. In this article, we propose a novel method of selecting subject and class specific frequency bands based on the analysis of a channel‐frequency matrix, which we call a channel‐frequency map. We operate the classification process for each frequency band individually, i.e., spatial filtering, feature extraction, and classification, and determine a class label for an input EEG by considering the outputs from multiple classifiers together at the end. From our experiments on a public dataset of BCI Competition IV (2008) II‐a that includes four motor imagery tasks from nine subjects, the proposed algorithm outperformed the common spatial pattern (CSP) algorithm in a broad band and a filter bank CSP algorithm on average in terms of cross‐validation and session‐to‐session transfer rate. Furthermore, a considerable increase of classification accuracy has been achieved for certain subjects. We also would like to note that the proposed data‐driven frequency bands selection method is applicable to other kinds of single‐trial EEG classifications that are based on modulations of brain rhythms, by no means limited to motor imagery‐based BCI applications. © 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 123–130, 2011

Event-related desynchronization of motor cortical oscillations in patients with multiple system atrophy

Multiple system atrophy (MSA) is a progressive neurodegenerative disease characterized by parkinsonism (MSA-P), cerebellar and autonomic deficits. In Parkinson's disease (PD), an impaired modulation of motor cortical mu and beta range oscillations may be related to the pathophysiology of bradykinesia. Event-related desynchronization (ERD) of these oscillations occur for 1-2s preceding a voluntary movement in normal subjects and patients with PD treated with levodopa while only lasting around 0.5s in untreated patients. Motor cortical rhythms were recorded from subdural strip electrodes in three patients with MSA-P while taking their regular dopaminergic medications. Following a ready cue, patients performed an externally cued wrist extension movement to a go cue. In addition, recordings were obtained during imagined wrist extension movements to the same cues and during self-paced wrist extensions. ERD and event-related synchronization were examined in subject-specific frequency bands. All patients showed movement-related ERD in subject-specific frequency bands below ~40Hz in both externally cued and self-paced conditions. Preparatory ERD latency preceding self-cued movement was 900ms in one patient and at or after movement onset in the other two patients. In the externally cued task, a short lasting (<1.3s) ready cue-related ERD that was not sustained to movement onset was observed in two patients. Imagined movements resulted in go cue-related ERD with a smaller magnitude in the same two patients. These results indicate that the modulation of motor cortical oscillations in patients with MSA that are treated with levodopa is similar to that occurring in untreated patients with PD. The findings suggest that cortical activation in patients with MSA is diminished, may be related to pathophysiological changes occurring in the basal ganglia and correlates with the poor clinical response that these patients typically obtain with dopaminergic therapy.

A Time-Frequency Approach to Feature Extraction for a Brain-Computer Interface with a Comparative Analysis of Performance Measures

The paper presents an investigation into a time-frequency (TF) method for extracting features from the electroencephalogram (EEG) recorded from subjects performing imagination of left- and right-hand movements. The feature extraction procedure (FEP) extracts frequency domain information to form features whilst time-frequency resolution is attained by localising the fast Fourier transformations (FFTs) of the signals to specific windows localised in time. All features are extracted at the rate of the signal sampling interval from a main feature extraction (FE) window through which all data passes. Subject-specific frequency bands are selected for optimal feature extraction and intraclass variations are reduced by smoothing the spectra for each signal by an interpolation (IP) process. The TF features are classified using linear discriminant analysis (LDA). The FE window has potential advantages for the FEP to be applied in an online brain-computer interface (BCI). The approach achieves good performance when quantified by classification accuracy (CA) rate, information transfer (IT) rate, and mutual information (MI). The information that these performance measures provide about a BCI system is analysed and the importance of this is demonstrated through the results.