Upper Limb Movement Execution Classification using Electroencephalography for Brain Computer Interface.

An accurate classification of upper limb movements using electroencephalogram (EEG) signals is gaining significant importance in recent years due to the prevalence of brain-computer interfaces. The upper limbs in the human body are crucial since different skeletal segments combine to make a range of motions that helps us in our trivial daily tasks. Decoding EEG-based upper limb movements can be of great help to people with spinal cord injury (SCI) or other neuro-muscular diseases such as amyotrophic lateral sclerosis (ALS), primary lateral sclerosis, and periodic paralysis. This can manifest in a loss of sensory and motor function, which could make a person reliant on others to provide care in day-to-day activities. We can detect and classify upper limb movement activities, whether they be executed or imagined using an EEG-based brain-computer interface (BCI). Toward this goal, we focus our attention on decoding movement execution (ME) of the upper limb in this study. For this purpose, we utilize a publicly available EEG dataset that contains EEG signal recordings from fifteen subjects acquired using a 61-channel EEG device. We propose a method to classify four ME classes for different subjects using spectrograms of the EEG data through pre-trained deep learning (DL) models. Our proposed method of using EEG spectrograms for the classification of ME has shown significant results, where the highest average classification accuracy (for four ME classes) obtained is 87.36%, with one subject achieving the best classification accuracy of 97.03%.Clinical relevance- This research shows that movement execution of upper limbs is classified with significant accuracy by employing a spectrogram of the EEG signals and a pre-trained deep learning model which is fine-tuned for the downstream task.

Evaluation of restoration of sensitivities of resistant Staphylococcus aureus isolates by using cefuroxime and clavulanic acid in combination.

BACKGROUND: The present study was planned to observe the activity of cefuroxime, a second generation cephalosporin after combining it with a beta-lactamase inhibitor calvulanic acid. The study was conducted to evaluate the restoration or increase in sensitivity of beta-lactamase producing isolates of Staphylococcus aureus. METHODS: Staphylococcus aureus were identified by standard procedures. For beta-lactamase detection chromogenic Nitrocefin impregnated sticks were used. The sensitivity of the bacteria to the antibiotic disks was measured by disk diffusion method using standard zone diameter criteria given by National Committee of Clinical Laboratory Standards. RESULTS: The disks of cefuroxime with clavulanic acid had developed larger zones of inhibition. The activity of cefuroxime against Staphylococcus areus was significantly increased by clavulanic acid. CONCLUSION: Clavulanic acid if used in combination with cefuroxime, can improve the antimicrobial activity of cefuroxime against beta-lactamase producing Staphylococcus aureus.