Publications by Serafeim Perdikis

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This work studies the class of algorithms for learning with side-information that emerges by extending generative models with embedded context-related variables. Using finite mixture models (FMMs) as the prototypical Bayesian network, we show that maximum- ...

Behavioral assessments of consciousness based on overt command following cannot differentiate patients with disorders of consciousness (DOC) from those who demonstrate a dissociation between intent/awareness and motor capacity: cognitive motor dissociation ...

OBJECTIVES: Brain-computer interfaces (BCIs) are no longer only used by healthy participants under controlled conditions in laboratory environments, but also by patients and end-users, controlling applications in their homes or clinics, without the BCI exp ...

Hand sensorimotor impairments are among the most common consequences of injuries affecting the central and peripheral nervous systems, leading to a drastic reduction in the quality of life for affected individuals. Combining wearable robotic exoskeletons a ...

Introduction Diagnosis and prediction of recovery in the acute phase of disorders of consciousness (DOC) are critical for subsequent medical decisions. However, reliable assessment remains elusive due to the inability of current validated clinical scales t ...

To investigate whether a motor attempt EEG paradigm coupled with functional electrical stimulation can detect command following and, therefore, signs of conscious awareness in patients with disorders of consciousness, we recorded nine patients admitted to ...

Successful operation of motor imagery (MI)-based brain-computer interfaces (BCI) requires mutual adaptation between the human subject and the BCI. Traditional training methods, as well as more recent ones based on co-adaptation, have mainly focused on the ...

Brain-Computer Interfaces (BCIs) are no longer only used by healthy subjects under control conditions in laboratory environments, but by patients controlling applications at their homes, without the BCI experts around. But are the technology and the field ...

Motor imagery (MI) has been largely studied as a way to enhance motor learning and to restore motor functions. Although it is agreed that users should emphasize kinesthetic imagery during MI, recordings of MI brain patterns are not sufficiently reliable fo ...

Objective. This work presents a first motor imagery-based, adaptive brain–computer interface (BCI) speller, which is able to exploit application-derived context for improved, simultaneous classifier adaptation and spelling. Online spelling experiments with ...

The non-invasive Brain-Computer Interface (BCI) developed in our lab targets asynchronous operation of devices by monitoring electroencephalographic (EEG) activity and identifying oscillatory patterns that the user can voluntary modulate through the execut ...

Brain-machine interfaces (BMI) have largely been demonstrated in laboratory conditions involving, mainly, healthy users. We have recently carried out a series of studies with a substantial number of motor-disabled end-users operating different brain-contro ...

Objective: This study investigated the effect of multimodal (visual and auditory) continuous feedback with information about the uncertainty of the input signal on motor imagery based BCI performance. A liquid floating through a visualization of a funnel ( ...

One of the problems of non-invasive BCI applications is the noise and change of EEG recordings that can result in performance degradation. Signal noise and changes can for instance be the result of electrode misplacements, impedances degradation or complet ...

One important source of performance degradation in BCIs is bias towards one of the mental classes. Recent literature has focused on the general problem of classification accuracy drop, identifying non-stationarity as the generating factor, thus leading to ...

This work aims at corroborating the importance and efficacy of mutual learning in motor imagery (MI) brain–computer interface (BCI) by leveraging the insights obtained through our participation in the BCI race of the Cybathlon event. We hypothesized that, ...

Objective. While brain-computer interfaces (BCIs) for communication have reached considerable technical maturity, there is still a great need for state-of-the-art evaluation by end-users outside laboratory environments. To achieve this primary objective, i ...

Motor-disabled end users have successfully driven a telepresence robot in a complex environment using a Brain-Computer Interface (BCI). However, to facilitate the interaction aspect that underpins the notion of telepresence, users must be able to voluntari ...

Serafeim Perdikis

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Context-Aware Learning for Generative Models

Peri-personal space encoding in patients with disorders of consciousness and cognitive-motor dissociation

Transferring brain-computer interfaces beyond the laboratory: Successful application control for motor-disabled users

mano: A Wearable Hand Exoskeleton for Activities of Daily Living and Neurorehabilitation

EEG paradigms as a supplemental tool to behavioral assessments of DOC

Motor Attempt EEG Paradigm As a Diagnostic Tool for Disorders of Consciousness

Subject-oriented training for motor imagery brain-computer interfaces

Are we ready? Issues in transferring BCI technology from experts to users

Sensory threshold neuromuscular electrical stimulation fosters motor imagery performance

Context-aware adaptive spelling in motor imagery BCI

Evidence Accumulation in asynchronous BCI

Brain-Machine Interfaces: Progress in Large Clinical Validation

The effect of multimodal and enriched feedback on SMR-BCI performance

Toward online detection of BCI performance degradation

A supervised recalibration protocol for unbiased BCI

The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users

Mutual learning and spelling in motor imagery brain-computer interfaces

Brain Racers: How paralysed athletes used a Brain-Computer Interface to win gold at the cyborg olympics

Clinical evaluation of BrainTree, a motor imagery hybrid BCI speller

A Hybrid BCI for Enhanced Control of a Telepresence Robot