In this work, an algorithm for the classification of six motor functions from an electroencephalogram (EEG) signal that combines a common spatial pattern (CSP) filter and a continuous wavelet transform (CWT), is investigated. The EEG data comprise six grasp-and-lift events, which are used to investigate the potential of using EEG as input signals with brain computer interface devices for controlling prosthetic devices for upper limb movement. Selected EEG channels are the ones located over the motor cortex, C3, Cz and C4, as well as at the parietal region, P3, Pz and P4. In general, the proposed algorithm includes three main stages, band pass filtering, CSP filtering, and wavelet transform and training on GoogLeNet for feature extraction, feature learning and classification. The band pass filtering is performed to select the EEG signal in the band of 7 Hz to 30 Hz while eliminating artifacts related to eye blink, heartbeat and muscle movement. The CSP filtering is applied on two-class EEG signals that will result in maximizing the power difference between the two-class dataset. Since CSP is mathematically developed for two-class events, the extension to the multiclass paradigm is achieved by using the approach of one class versus all other classes. Subsequently, continuous wavelet transform is used to convert the band pass and CSP filtered signals from selected electrodes to scalograms which are then converted to images in grayscale format. The three scalograms from the motor cortex regions and the parietal region are then combined to form two sets of RGB images. Next, these RGB images become the input to GoogLeNet for classification of the motor EEG signals. The performance of the proposed classification algorithm is evaluated in terms of precision, sensitivity, specificity, accuracy with average values of 94.8%, 93.5%, 94.7%, 94.1%, respectively, and average area under the receiver operating characteristic (ROC) curve equal to 0.985. These results indicate a good performance of the proposed algorithm in classifying grasp-and-lift events from EEG signals.
The antibacterial nature of graphene oxide (GO) has stimulated wide interest in the medical field. Although the antibacterial activity of GO towards bacteria has been well studied, a deeper understanding of the mechanism of action of GO is still lacking. The objective of the study was to elucidate the difference in the interactions of GO towards Gram-positive and Gram-negative bacteria. The synthesized GO was characterized by Ultraviolet-visible spectroscopy (UV-vis), Raman and Attenuated Total Reflectance-Fourier-transform infrared spectroscopy (ATR-FTIR). Viability, time-kill and Lactose Dehydrogenase (LDH) release assays were carried out along with FESEM, TEM and ATR-FTIR analysis of GO treated bacterial cells. Characterizations of synthesized GO confirmed the transition of graphene to GO and the antibacterial activity of GO was concentration and time-dependent. Loss of membrane integrity in bacteria was enhanced with increasing GO concentrations and this corresponded to the elevated release of LDH in the reaction medium. Surface morphology of GO treated bacterial culture showed apparent differences in the mechanism of action of GO towards Gram-positive and Gram-negative bacteria where cell entrapment was mainly observed for Gram-positive Staphylococcus aureus and Enterococcus faecalis whereas membrane disruption due to physical contact was noted for Gram-negative Escherichia coli and Pseudomonas aeruginosa. ATR-FTIR characterizations of the GO treated bacterial cells showed changes in the fatty acids, amide I and amide II of proteins, peptides and amino acid regions compared to untreated bacterial cells. Therefore, the data generated further enhance our understanding of the antibacterial activity of GO towards bacteria.