Prosthetic motor imaginary task classification based on EEG quality assessment features

Brain Computer Interface (BCI) plays an important role in the communication between human and machines. This communication is based on the human brain signals. In these systems, users use their brain instead of the limbs or body movements to do tasks. The brain signals are analyzed and translated into commands to control any communication devices, robots or computers. In this paper, the aim was to enhance the performance of a brain computer interface (BCI) systems through better prosthetic motor imaginary tasks classification. The challenging part is to use only a single channel of electroencephalography (EEG). Arm movement imagination is the task of the user, where (s)he was asked to imagine moving his arm up or down. Our system detected the imagination based on the input brain signal. Some EEG quality features were extracted from the brain signal, and the Decision Tree was used to classify the participant's imagination based on the extracted features. Our system is online which means that it can give the decision as soon as the signal is given to the system (takes only 20 ms). Also, only one EEG channel is used for classification which reduces the complexity of the system which leads to fast performance. Hundred signals were used for testing, on average 97.4% of the up-down prosthetic motor imaginary tasks were detected correctly. This method can be used in many different applications such as: moving artificial limbs and wheelchairs due to it's high speed and accuracy.

EEG signal analysis for BCI application using fuzzy system

An approach to EEG signal classification for brain-computer interface (BCI) application using fuzzy standard additive model is introduced in this paper. The Wilcoxon test is employed to rank wavelet coefficients. Top ranking wavelets are used to form a feature set that serves as inputs to the fuzzy classifiers. Experiments are carried out using two benchmark datasets, Ia and Ib, downloaded from the BCI competition II. Prevalent classifiers including feedforward neural network, support vector machine, k-nearest neighbours, ensemble learning Adaboost and adaptive neuro-fuzzy inference system are also implemented for comparisons. Experimental results show the dominance of the proposed method against competing approaches.

Single pulse TMS-EEG reveals no electrophysiological abnormality in adults with high-functioning Autism Spectrum Disorder

 Objective: Neuroimaging and electrophysiological research have revealed a range of neural abnormalities in autism spectrum disorder (ASD), but a comprehensive understanding remains elusive. We utilized a novel methodology among individuals with ASD and matched controls, combining transcranial magnetic stimulation (TMS) with concurrent electroencephalogram (EEG) recording (TMS-EEG) to explore cortical function and connectivity in three sites implicated in the neuropathophysiology of ASD (dorsolateral prefrontal cortex, primary motor cortex, and temporoparietal junction). As there is evidence for neurobiological gender differences in ASD, we also examined the influence of biological sex.

Methods: TMS pulses were applied to each of the three sites (right lateralized) during 20-channel EEG recording.

Results: We did not identify any differences in the EEG response to TMS between ASD and control groups. This finding remained when data were stratified by sex. Nevertheless, traits and characteristics associated with ASD were correlated with the neurophysiological response to TMS.

Conclusion: While TMS-EEG did not appear to clarify the neuropathophysiology of ASD, the relationships identified between the neurophysiological response to TMS and clinical characteristics warrant further investigation.

Distribution entropy analysis of epileptic EEG signals

It is an open-ended challenge to accurately detect the epileptic seizures through electroencephalogram (EEG) signals. Recently published studies have made elaborate attempts to distinguish between the normal and epileptic EEG signals by advanced nonlinear entropy methods, such as the approximate entropy, sample entropy, fuzzy entropy, and permutation entropy, etc. Most recently, a novel distribution entropy (DistEn) has been reported to have superior performance compared with the conventional entropy methods for especially short length data. We thus aimed, in the present study, to show the potential of DistEn in the analysis of epileptic EEG signals. The publicly-accessible Bonn database which consisted of normal, interictal, and ictal EEG signals was used in this study. Three different measurement protocols were set for better understanding the performance of DistEn, which are: i) calculate the DistEn of a specific EEG signal using the full recording; ii) calculate the DistEn by averaging the results for all its possible non-overlapped 5 second segments; and iii) calculate it by averaging the DistEn values for all the possible non-overlapped segments of 1 second length, respectively. Results for all three protocols indicated a statistically significantly increased DistEn for the ictal class compared with both the normal and interictal classes. Besides, the results obtained under the third protocol, which only used very short segments (1 s) of EEG recordings showed a significantly (p <; 0.05) increased DistEn for the interictal class in compassion with the normal class, whereas both analyses using relatively long EEG signals failed in tracking this difference between them, which may be due to a nonstationarity effect on entropy algorithm. The capability of discriminating between the normal and interictal EEG signals is of great clinical relevance since it may provide helpful tools for the detection of a seizure onset. Therefore, our study suggests that the DistEn analysis of EEG signals is very promising for clinical and even portable EEG monitoring.

Classification of 5-S epileptic EEG recordings using distribution entropy and sample entropy

Epilepsy is an electrophysiological disorder of the brain, the hallmark of which is recurrent and unprovoked seizures. Electroencephalogram (EEG) measures electrical activity of the brain that is commonly applied as a non-invasive technique for seizure detection. Although a vast number of publications have been published on intelligent algorithms to classify interictal and ictal EEG, it remains an open question whether they can be detected using short-length EEG recordings. In this study, we proposed three protocols to select 5 s EEG segment for classifying interictal and ictal EEG from normal. We used the publicly-accessible Bonn database, which consists of normal, interical, and ictal EEG signals with a length of 4097 sampling points (23.6 s) per record. In this study, we selected three segments of 868 points (5 s) length from each recordings and evaluated results for each of them separately. The well-studied irregularity measure-sample entropy (SampEn)-and a more recently proposed complexity measure-distribution entropy (DistEn)-were used as classification features. A total of 20 combinations of input parameters m and τ for the calculation of SampEn and DistEn were selected for compatibility. Results showed that SampEn was undefined for half of the used combinations of input parameters and indicated a large intra-class variance. Moreover, DistEn performed robustly for short-length EEG data indicating relative independence from input parameters and small intra-class fluctuations. In addition, it showed acceptable performance for all three classification problems (interictal EEG from normal, ictal EEG from normal, and ictal EEG from interictal) compared to SampEn, which showed better results only for distinguishing normal EEG from interictal and ictal. Both SampEn and DistEn showed good reproducibility and consistency, as evidenced by the independence of results on analysing protocol.

Behavioural differences between EEG-defined subgroups of children with Attention-Deficit/Hyperactivity Disorder

Objective: This study investigated the presence and nature of EEG clusters within a clinically-referred sample of children with Attention-Deficit/Hyperactivity Disorder (AD/HD), and whether behavioural differences exist between clusters.

Method: Participants were 155 boys with AD/HD and 109 age- and gender-matched controls. EEG was recorded during an eyes-closed resting condition and Fourier transformed to provide estimates for total power, and relative delta, theta, alpha, and beta. EEG data were grouped into 3 regions, and subjected to Cluster Analysis. Behavioural data for each cluster were compared against the remaining AD/HD subjects.

Results: Four EEG clusters were found. These were characterised by (a) elevated beta activity, (b) elevated theta with deficiencies of alpha and beta, (c) elevated slow wave with less fast wave activity, and (d) elevated alpha. An exploratory analysis of behavioural correlates with these EEG subtypes indicated the presence of interesting trends that need further investigation.

Conclusions: This study found that the AD/HD EEG profiles reported in past studies are robust and not substantially affected by the inclusion of children with other comorbid conditions. The observed group differences in behavioural profiles indicated that different patterns of EEG activity have importance in determining behaviour.

Significance: This is the first study to link behavioural profiles of children with AD/HD to specific EEG abnormalities.

Signal processing for automated EEG quality assessment

An automated signal quality assessment method was proposed for the EEG signals, which will help in testing new BCI algorithms so that the testing can be made on high quality signals only. This research includes the development of novel feature extraction technique and a new clustering algorithm for EEG signals.