Non-Invasive Brain Computer Interface for Mental Control of a Simulated Wheelchair

This poster presents results obtained from experiments of driving a brain-actuated simulated wheelchair that incorporates the shared-control intelligence method. The simulated wheelchair is controlled offline using band power features. The task is to drive the wheelchair along a corridor avoiding two obstacles. We have analyzed data from 4 na�ve subjects during 25 sessions carried out in two days. To measure the performance of the brain-actuated wheelchair we have compared the final position of the wheelchair with the end point of the desired trajectory. The experiments show that the incorporation of a higher intelligence level in the control device significantly helps the subject to drive the robot device

Acute Administration of the GLP-1 Receptor Agonist Lixisenatide Diminishes Postprandial Insulin Secretion in Healthy Subjects But Not in Type 2 Diabetes, Associated with Slowing of Gastric Emptying

Published online 22 April 2022Introduction: It is uncertain whether lixisenatide has postprandial insulinotropic effects when its effect on slowing gastric emptying is considered, in healthy subjects and type 2 diabetes mellitus (T2DM). We evaluated the effects of single administration of 10 lg sc lixisenatide on glycaemia, insulin secretion and gastric emptying (GE), measured using the ‘gold standard’ technique of scintigraphy following an oral glucose load (75 g glucose). Methods: Fifteen healthy subjects (nine men, six women; age 67.2 ± 2.3 years) and 15 patients with T2DM (nine men, six women; age 61.9 ± 2.3 years) had measurements of GE, plasma glucose, insulin and C-peptide for 180 min after a radiolabeled 75 g glucose drink on two separate days. All subjects received lixisenatide (10 lg sc) or placebo in a randomised, double-blind, crossover fashion 30 min before the drink. Insulin secretory response (ISR) was determined using the C-peptide deconvolution method. Results: GE was markedly slowed by lixisenatide compared with placebo in both healthy subjects (1.45 ± 0.10 kcal/min for placebo vs. 0.60 ± 0.14 kcal/min for lixisenatide) and diabetes (1.57 ± 0.06 kcal/min for placebo vs. 0.75 ± 0.13 kcal/min for lixisenatide) (both P\0.001) with no difference between the two groups (P = 0.42). There was a moderate to strong inverse correlation between the early insulin secretory response calculated at 60 min and gastric retention at 60 min with lixisenatide treatment in healthy subjects (r = - 0.8, P = 0.0003) and a trend in type 2 diabetes (r = - 0.4, P = NS), compared with no relationships in the placebo arms (r = - 0.02, P = NS, healthy subjects) and (r = - 0.16, P = NS, type 2 diabetes). Conclusion: The marked slowing of GE of glucose induced by lixisenatide is associated with attenuation in the rise of postprandial glucose in both healthy subjects and diabetes and early insulin secretory response in healthy subjects.Chinmay S. Marathe . Hung Pham . Tongzhi Wu . Laurence G. Trahair . Rachael S. Rigda . Madeline D. M. Buttfield . Seva Hatzinikolas . Kylie Lange . Christopher K. Rayner . Andrea Mari . Michael Horowitz . Karen L. Jone

Adaptive learning with covariate shift-detection for motor imagery-based brain–computer interface

A common assumption in traditional supervised learning is the similar probability distribution of data between the training phase and the testing/operating phase. When transitioning from the training to testing phase, a shift in the probability distribution of input data is known as a covariate shift. Covariate shifts commonly arise in a wide range of real-world systems such as electroencephalogram-based brain–computer interfaces (BCIs). In such systems, there is a necessity for continuous monitoring of the process behavior, and tracking the state of the covariate shifts to decide about initiating adaptation in a timely manner. This paper presents a covariate shift-detection and -adaptation methodology, and its application to motor imagery-based BCIs. A covariate shift-detection test based on an exponential weighted moving average model is used to detect the covariate shift in the features extracted from motor imagery-based brain responses. Following the covariate shift-detection test, the methodology initiates an adaptation by updating the classifier during the testing/operating phase. The usefulness of the proposed method is evaluated using real-world BCI datasets (i.e. BCI competition IV dataset 2A and 2B). The results show a statistically significant improvement in the classification accuracy of the BCI system over traditional learning and semi-supervised learning methods

Biomechanical characteristics of handballing maximally in Australian football

The handball pass is influential in Australian football, and achieving higher ball speeds in flight is an advantage in increasing distance and reducing the chance of interceptions. The purpose of this study was to provide descriptive kinematic data and identify key technical aspects of maximal handball performance. Three-dimensional full body kinematic data from 19 professional Australian football players performing handball pass for maximal speed were collected, and the hand speed at ball contact was used to determine performance. Sixty-four kinematic parameters initially obtained were reduced to 15, and then grouped into like components through a two-stage supervised principal components analysis procedure. These components were then entered into a multiple regression analysis, which indicated that greater hand speed was associated with greater shoulder angular velocity and separation angle between the shoulders and pelvis at ball contact, as well as an earlier time of maximum upper-trunk rotation velocity. These data suggested that in order to increase the speed of the handball pass in Australian football, strategies like increased shoulder angular velocity, increased separation angle at ball contact, and earlier achievement of upper-trunk rotation speed might be beneficial

HIGH FREQUENCY BANDS AND ESTIMATED LOCAL FILED POTENTIALS TO IMPROVE SINGLE-TRIAL CLASSIFICATION OF ELECTROENCEPHALOGRAPHIC SIGNALS

SUMMARY: Non-invasive brain-computer interfaces are traditionally based on slow, mu or beta rhythms. However, there is mounting evidence that neural oscillations up to 200 Hz play important roles in processes such as attention, perception, motor action and conscious experience. In this preliminary study we propose to extend the investigations to the complete frequency spectrum and to compare the high frequency bands with the usual low frequencies. It appears that the 70– 130 Hz band and the 170-230 Hz band performs better than the traditional 2–40 Hz band. In a second step we applied the same analysis to the estimated local field potentials from the scalp EEG. The same frequency bands show the best performances, and the use of eLFP leads to an increase of performances of ∼5%