Altered task induced functional brain networks and small-world properties in autism.

Introduction: Facial emotion recognition (FER) requires the integration of multi-dimensional information across various brain regions. Autistic individuals commonly experience difficulties in FER, a phenomenon often attributed to differences in brain connectivity. The nature of task-induced functional brain networks could provide insight into the neuromechanisms underlying FER difficulties in autism, however, to date, these mechanisms remain poorly understood.

Design Considerations for Long Term Non-invasive Brain Computer Interface Training With Tetraplegic CYBATHLON Pilot.

Several studies in the recent past have demonstrated how Brain Computer Interface (BCI) technology can uncover the neural mechanisms underlying various tasks and translate them into control commands. While a multitude of studies have demonstrated the theoretic potential of BCI, a point of concern is that the studies are still confined to lab settings and mostly limited to healthy, able-bodied subjects. The CYBATHLON 2020 BCI race represents an opportunity to further develop BCI design strategies for use in real-time applications with a tetraplegic end user.

BCI for stroke rehabilitation: motor and beyond.

Stroke is one of the leading causes of long-term disability among adults and contributes to major socio-economic burden globally. Stroke frequently results in multifaceted impairments including motor, cognitive and emotion deficits. In recent years, brain-computer interface (BCI)-based therapy has shown promising results for post-stroke motor rehabilitation.

Wavlet phase-locking based binary classification of hand movement directions from EEG.

Objective: Brain signals can be used to extract relevant features to decode various limb movement parameters such as the direction of upper limb movements. Amplitude based feature extraction techniques have been used to study such motor activity of upper limbs whereas phase synchrony, used to estimate functional relationship between signals, has rarely been used to study single hand movements in different directions.

Approach: In this paper, a novel phase-locking-based feature extraction method, called wavelet phase-locking value (W-PLV) is proposed to analyse synchronous EEG channel-pairs and classify hand movement directions.