Subject-specific Frequency Research Articles

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.

Contribution of inter-muscular synchronization in the modulation of tremor intensity in Parkinson's disease.

BackgroundInvoluntary central oscillations at single and double tremor frequencies drive the peripheral neuromechanical system of muscles and joints to cause tremor in Parkinson’s disease (PD). The central signal of double tremor frequency was found to correlate more directly to individual muscle EMGs (Timmermann et al. 2003). This study is aimed at investigating what central components of oscillation contribute to inter-muscular synchronization in a group of upper extremity muscles during tremor in PD patients.Methods11 idiopathic, tremor dominant PD subjects participated in this study. Joint kinematics during tremor in the upper extremity was recorded along with EMGs of six upper arm muscles using a novel experimental apparatus. The apparatus provided support for the upper extremity on a horizontal surface with reduced friction, so that resting tremor in the arm can be recorded with a MotionMonitor II system. In each subject, the frequencies of rhythmic firings in upper arm muscles were determined using spectral analysis. Paired and pool-averaged coherence analyses of EMGs for the group of muscles were performed to correlate the level of inter-muscular synchronization to tremor amplitudes at shoulder and elbow. The phase shift between synchronized antagonistic muscle pairs was calculated to aid coherence analysis in the muscle pool.ResultsRecorded EMG revealed that rhythmic firings were present in most recorded muscles, which were either synchronized to form phase-locked bursting cycles at a subject specific frequency, or unsynchronized with a random phase distribution. Paired coherence showed a stronger synchronization among a subset of recorded arm muscles at tremor frequency than that at double tremor frequency. Furthermore, the number of synchronized muscles in the arm was positively correlated to tremor amplitudes at elbow and shoulder. Pool-averaged coherence at tremor frequency also showed a better correlation with the amplitude of resting tremor than that of double tremor frequency, indicating that the neuromechanical coupling in peripheral neuromuscular system was stronger at tremor frequency.ConclusionsBoth paired and pool-averaged coherences are more consistent indexes to correlate to tremor intensity in a group of upper extremity muscles of PD patients. The central drive at tremor frequency contributes mainly to synchronize peripheral muscles in the modulation of tremor intensity.

Impact of head modeling and sensor types in localizing human gamma-band oscillations.

An effective mechanism in neuronal communication is oscillatory neuronal synchronization. The neuronal gamma-band (30-100 Hz) synchronization is associated with attention which is induced by a certain visual stimuli. Numerous studies have shown that the gamma-band activity is observed in the visual cortex. However, impact of different head modeling techniques and sensor types to localize gamma-band activity have not yet been reported. To do this, the brain activity was recorded using 306 magnetoencephalography (MEG) sensors, consisting of 102 magnetometers and 102 pairs of planar gradiometers (one measuring the derivative of the magnetic field along the latitude and the other along the longitude), and the data were analyzed with respect to time, frequency, and location of the strongest response. The spherical head models with a single-shell and overlapping spheres (local sphere) have been used as a forward model for calculating the external magnetic fields generated from the gamma-band activity. For each sensor type, the subject-specific frequency range of the gamma-band activity was obtained from the spectral analysis. The identified frequency range of interest with the highest gamma-band activity is then localized using a spatial-filtering technique known as dynamic imaging of coherent sources (DICS). The source analysis for all the subjects revealed that the gradiometer sensors which measure the derivative along the longitude, showed sources close to the visual cortex (cuneus) as compared to the other gradiometer sensors which measure the derivative along the latitude. However, using the magnetometer sensors, it was not possible to localize the sources in the region of interest. When comparing the two head models, the local-sphere model helps in localizing the source more focally as compared to the single-shell head model.

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

Adaptive tracking of discriminative frequency components in electroencephalograms for a robust brain–computer interface

In an electroencephalogram (EEG)-based brain–computer interface (BCI), motor imagery has been successfully used as a communication strategy. Motor imagery causes detectable amplitude changes in certain frequency bands of EEGs, which are dubbed event-related desynchronization\\synchronization. The frequency components that give effective discrimination between different types of motor imagery are subject specific and identification of these subject-specific discriminative frequency components (DFCs) is important for the accurate classification of motor imagery activities. In this paper, we propose a new method to estimate the DFC using the Fisher criterion and investigate the variability of these DFCs over multiple sessions of EEG recording. Observing the variability of DFC over sessions in the analysis, a new BCI approach called the Adaptively Weighted Spectral-Spatial Patterns (AWSSP) algorithm is proposed. AWSSP tracks the variation in DFC over time adaptively based on the deviation of discriminative weight values of frequency components. The classification performance of the proposed AWSSP is compared with a static BCI approach that employs fixed DFCs. In the offline and online experiments, AWSSP offers better classification performance than the static approach, emphasizing the significance of tracking the variability of DFCs in EEGs for developing robust motor imagery-based BCI systems. A study of the effect of feedback on the variation in DFCs is also performed in online experiments and it is found that the presence of visual feedback results in increased variation in DFCs.

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.

Optimizing EEG Channel Selection by Regularized Spatial Filtering and Multi Band Signal Decomposition

Appropriate choice of number of electrodes and their positions are essential in Brain-Computer Interface applications since using less electrodes collects insufficient information for classification purposes whereas using more collects redundant information that could degrade BCI performance. This paper proposes a novel method of optimizing EEG channel selection by using the regularized Common Spatial Pattern (CSP) algorithm to discard redundant channels and multi band signal decomposition to select subject-specific frequency range. The performance of the proposed method is compared with EEG channel selection using Fisher criterion, mutual information, support vector and CSP on 9 subjects for two motor imagery tasks. Experiment results show the proposed method yields the highest accuracy in selecting 4 to 10 channels compared with the methods studied as well as using all the channels. The results also illustrate the proposed method significantly improves by multi band filtering and can achieve an average of 47% reduction of channels with only an averaged drop of 1.04% in classification accuracy.

A New Discriminative Common Spatial Pattern Method for Motor Imagery Brain–Computer Interfaces

Event-related desynchronization/synchronization patterns during right/left motor imagery (MI) are effective features for an electroencephalogram-based brain-computer interface (BCI). As MI tasks are subject-specific, selection of subject-specific discriminative frequency components play a vital role in distinguishing these patterns. This paper proposes a new discriminative filter bank (FB) common spatial pattern algorithm to extract subject-specific FB for MI classification. The proposed method enhances the classification accuracy in BCI competition III dataset IVa and competition IV dataset IIb. Compared to the performance offered by the existing FB-based method, the proposed algorithm offers error rate reductions of 17.42% and 8.9% for BCI competition datasets III and IV, respectively.

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.

Enhancement of Left-Right Sensorimotor EEG Differences During Feedback-Regulated Motor Imagery

EEG feedback studies demonstrate that human subjects can learn to regulate electrocortical activity over the sensorimotor cortex. Such self-induced EEG changes could serve as control signals for a Brain Computer Interface. The experimental task of the current study was to imagine either right-hand or left-hand movement depending on a visual cue stimulus on a computer monitor. The performance of this imagination task was controlled on-line by means of a feedback bar that represented the current EEG pattern. EEG signals recorded from left and right central recording sites were used for on-line classification. For the estimation of EEG parameters, an adaptive autoregressive model was applied, and a linear discriminant classifier was used to discriminate between EEG patterns associated with left and right motor imagery. Four trained subjects reached 85% to 95% classification accuracy in the course of the experimental sessions. To investigate the impact of continuous feedback presentation, time courses of band power changes were computed for subject-specific frequency bands. The EEG data revealed a significant event-related desynchronization over the contralateral central area in all subjects. Two subjects simultaneously displayed synchronization of EEG activity (event-related synchronization) over the ipsilateral side. During feedback presentation the event-related desynchronization/event-related synchronization patterns showed increased hemispheric asymmetry compared to initial control sessions without feedback.

Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters.

Electroencephalogram (EEG) recordings during right and left motor imagery can be used to move a cursor to a target on a computer screen. Such an EEG-based brain-computer interface (BCI) can provide a new communication channel to replace an impaired motor function. It can be used by, e.g., patients with amyotrophic lateral sclerosis (ALS) to develop a simple binary response in order to reply to specific questions. Four subjects participated in a series of on-line sessions with an EEG-based cursor control. The EEG was recorded from electrodes overlying sensory-motor areas during left and right motor imagery. The EEG signals were analyzed in subject-specific frequency bands and classified on-line by a neural network. The network output was used as a feedback signal. The on-line error (100%-perfect classification) was between 10.0 and 38.1%. In addition, the single-trial data were also analyzed off-line by using an adaptive autoregressive (AAR) model of order 6. With a linear discriminant analysis the estimated parameters for left and right motor imagery were separated. The error rate obtained varied between 5.8 and 32.8% and was, on average, better than the on-line results. By using the AAR-model for on-line classification an improvement in the error rate can be expected, however, with a classification delay around 1 s.

Post-movement synchronization of beta rhythms in the EEG over the cortical foot area in man

Post-movement synchronization of the electroencephalogram (EEG) was studied in nine right-handed subjects who performed voluntary self-paced dorsal flexions with the right and left foot. The findings revealed that foot movement results in enhanced beta oscillations after movement. These beta bursts showed subject-specific resonance frequencies in the range between 12 and 32 Hz and were localized to electrode Cz and to one electrode 2.5 cm more anterior. Comparison of left and right foot movement revealed a larger post-movement beta synchronization (PMBS) with left foot movement, but no differences concerning the topographical distribution of the PMBS.

Post-movement synchronization of beta rhythms in the EEG over the cortical foot area in man

Post-movement synchronization of the electroencephalogram (EEG) was studied in nine right-handed subjects who performed voluntary self-paced dorsal flexions with the right and left foot. The findings revealed that foot movement results in enhanced beta oscillations after movement. These beta bursts showed subject-specific resonance frequencies in the range between 12 and 32 Hz and were localized to electrode Cz and to one electrode 2.5 cm more anterior. Comparison of left and right foot movement revealed a larger post-movement beta synchronization (PMBS) with left foot movement, but no differences concerning the topographical distribution of the PMBS.