EEG channel and feature investigation in binary and multiple motor imagery task predictions.

Introduction: Motor Imagery (MI) Electroencephalography (EEG) signals are non-stationary and dynamic physiological signals which have low signal-to-noise ratio. Hence, it is difficult to achieve high classification accuracy. Although various machine learning methods have already proven useful to that effect, the use of many features and ineffective EEG channels often leads to a complex structure of classifier algorithms.

Comparative efficacy of ultrasound-guided erector spinae plane block versus wound infiltration for postoperative analgesia in instrumented lumbar spinal surgeries.

Objective: This study compared the efficacy of ultrasound-guided erector spinae plane block (ESPB) and wound infiltration (WI) for postoperative analgesia in patients who underwent lumbar spinal surgery with instrumentation.

Methods: In this randomized controlled trial, 80 patients were divided into two groups: ESPB (n = 40) and WI (n = 40). Postoperative pain intensity was assessed via the visual analog scale (VAS) at multiple time points within 24 h.

EEG-based finger movement classification with intrinsic time-scale decomposition.

Introduction: Brain-computer interfaces (BCIs) are systems that acquire the brain's electrical activity and provide control of external devices. Since electroencephalography (EEG) is the simplest non-invasive method to capture the brain's electrical activity, EEG-based BCIs are very popular designs. Aside from classifying the extremity movements, recent BCI studies have focused on the accurate coding of the finger movements on the same hand through their classification by employing machine learning techniques.

Estimating Cognitive Load in a Mobile Personal Health Record Application: A Cognitive Task Analysis Approach.

Objectives: Mobile health applications that are designed without considering usability criteria can lead to cognitive overload, resulting in the rejection of these apps. To avoid this problem, the user interface of mobile health applications should be evaluated for cognitive load. This evaluation can contribute to the improvement of the user interface and help prevent cognitive overload for the user.

Subjective Cognitive Load Evaluation of a Mobile Personal Health Record Application.

Mobile Personal Health Records (mPHRs), which make it possible to track and manage users' health information, can be an important aid in improving people's health. Despite its potential benefits, poor usability of systems can hinder the adoption and use of mPHRs. This study aims to evaluate the usability of a mobile health application in terms of perceived cognitive workload and performance.