Serafeim Perdikis

University of Essex · School of Computer Science and Electronic Engineering

Mind-controlled wheelchairs are an intriguing assistive mobility solution applicable in complete paralysis. Despite progress in brain-machine interface (BMI) technology, its translation remains elusive. The primary objective of this study is to probe the hypothesis that BMI skill acquisition by end-users is fundamental to control a non-invasive bra...

Online coadaptive training has been successfully employed to enable people to control motor imagery (MI)-based brain-computer interfaces (BCIs), allowing to completely skip the lengthy and demotivating open-loop calibration stage traditionally applied before closed-loop control. However, practical reasons may often dictate to eventually switch off...

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-likelihood estimation (MLE) of parameters through expectation-maximization (EM) improves over...

Brain-machine interface (BMI) technology has rapidly matured over the last two decades, mainly thanks to the introduction of artificial intelligence (AI) methods, in particular, machine-learning algorithms. Yet, the need for subjects to learn to modulate their brain activity is a key component of successful BMI control. Blending machine and subject...

This chapter introduces the field of brain–machine interfaces (BMIs), also called brain–computer interfaces (BCIs), which has seen impressive achievements over the past few years. A BMI monitors the user’s brain activity, extracts specific features from the brain signals that reflect the intent of the subject, and translates them into actions. BMI...

Objective: Brain-computer interface (BCI) spelling is a promising communication solution for people in paralysis. Currently, BCIs suffer from imperfect decoding accuracy which calls for methods to handle spelling mistakes. Detecting error-related potentials (ErrPs) has been early identified as a potential remedy. Nevertheless, few works have studie...

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 (CMD). We argue that delineation of peri-personal space (PPS) - the multisensory-motor space...

Topography of CMC during right hand movement in healthy participants. (A) CMC in the alpha (8–12 Hz) frequency band. (B) CMC in the gamma (30–40 Hz) frequency band were also increased contralaterally during movement in the healthy participant group but to a lesser extent than the beta CMC. Color scale: CMC.

Topography of beta-range CMC from the single right-sided stroke patient. The data recorded show stronger left-sided and more locally focused beta CMC. (A) Beta CMC was lower over the affected right hemisphere during left hand movements than (B) over the left hemisphere during right-handed movements. Color scale: CMC.

Motor recovery following stroke is believed to necessitate alteration in functional connectivity between cortex and muscle. Cortico-muscular coherence has been proposed as a potential biomarker for post-stroke motor deficits, enabling a quantification of recovery, as well as potentially indicating the regions of cortex involved in recovery of funct...

Movements are preceded by certain brain states that can be captured through various neuroimaging techniques. Brain-Computer Interfaces can be designed to detect the movement intention brain state during driving, which could be beneficial in improving the interaction between a smart car and its driver, by providing assistance in-line with the driver...

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 acute rehabilitation after a brain lesion. We extracted peak classification accuracy and peak...

Brain-computer interfaces (BCI) are used in stroke rehabilitation to translate brain signals into intended movements of the paralyzed limb. However, the efficacy and mechanisms of BCI-based therapies remain unclear. Here we show that BCI coupled to functional electrical stimulation (FES) elicits significant, clinically relevant, and lasting motor r...

Author summary Noninvasive brain–computer interface (BCI) based on imagined movements can restore functions lost to disability by enabling spontaneous, direct brain control of external devices without risks associated with surgical implantation of neural interfaces. We hypothesized that, contrary to the popular trend of focusing on the machine lear...

Electrode configurations. (A) EEG channel configuration over 16 locations of the sensorimotor cortex according to the international 10–20 system. (B) EOG electrode configuration on the pilot’s right and left canthi, nasion, and forehead for the detection of ocular and facial muscle artifacts. EEG, electroencephalography; EOG, electrooculogram. (TIF...

BCI feature discriminancy for pilot P2 after artifact removal with FORCe. (A) Topographic maps of discriminancy per training month on the 16 EEG channel locations over the sensorimotor cortex monitored. Bright color indicates high discriminancy between Both Hands and Both Feet MI tasks employed by pilot P2. The discriminancy of each channel is quan...

User-training methodology details of the Cybathlon BCI race competitors. BCI, brain–computer interface. (DOCX)

BCI feature discriminancy maps for three typical BCI sessions of pilot P2 in August, September, and October after artifact removal with FORCe. Bright color indicates high discriminancy between Both Hands and Both Feet motor imagery tasks employed by pilot P2. The discriminancy of each feature (channel–frequency pair) is quantified as the Fisher sco...

BCI feature discriminancy maps per run (N) averaged for each training month. Bright color indicates high discriminancy between Both Hands and Both Feet MI tasks employed by both pilots (P1 top, P2 bottom). The discriminancy of each feature (channel-frequency pair) is quantified as the Fisher score of the EEG signal's power spectral density distribu...

BCI feature discriminancy per training modality. Topographic maps of discriminancy per training modality on the 16 EEG channel locations over the sensorimotor cortex monitored. Bright color indicates high discriminancy between Both Hands and Both Feet MI tasks employed by both pilots (P1 top, P2 bottom). The discriminancy of each channel is quantif...

BCI feature discriminancy maps per run (N) averaged for each training month. Bright color indicates high discriminancy between Both Hands and Both Feet motor imagery tasks employed by both pilots (P1 top, P2 bottom). The discriminancy of each feature (channel–frequency pair) is quantified as the Fisher score of the EEG signal's power spectral densi...

Training session information. The table presents the date of all executed training sessions for both pilots and the number and type of runs performed in each session and reported here. Asterisks indicate one or more runs have been lost due to technical failure or bad maintenance. (DOCX)

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 for many subjects. It has been suggested that the usage of somatosensory feedback would be more...

Hand sensorimotor impairments are among the most common consequences of cerebrovascular accidents and spinal cord injuries, leading to a drastic reduction in the quality of life for affected individuals. Combining wearable robotic exoskeletons and human-machine interfaces can be a promising avenue for the restoration and substitution of lost and im...

IN OCTOBER 2016, inside a sold-out arena in Zurich, a man named Numa Poujouly steered his wheelchair up to the central podium. As the Swiss national anthem played, organizers of the world's first cyborg Olympics hung a gold medal around Poujouly's neck. The 30-yearold, who became paralyzed after a bicycle accident in his teens, had triumphed in the...

Introduction: In stroke rehabilitation, one of the key components for motor improvement is brain plasticity and, in particular, the reestablishment of cortical and subcortical networks [1] that can be studied with connectivity analysis. Despite recent advances in brain-computer interface (BCI)-driven stroke therapy [2], it is still unclear what are...

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 ten able-bodied users evaluate the ability of our scheme, first, to alleviate non-stationari...

This work introduces algorithms able to exploit contextual information in order to improve maximum-likelihood (ML) parameter estimation in finite mixture models (FMM), demonstrating their benefits and properties in several scenarios. The proposed algorithms are derived in a probabilistic framework with regard to situations where the regular FMM gra...

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 (funnel feedback) provided enriched visual or enriched multimodal feedback. In a between subject design 30...

This chapter provides an overview of the functionality and the underlying principles of the brain-computer interfaces (BCI) developed by the Chair in Non-Invasive Brain-Machine Interface (CNBI) of the Swiss Federal Institute of Technology (EPFL), as well as exemplary applications where those have been successfully evaluated. Our laboratory mainly d...

One of the problems of non-invasive Brain- Computer Interface (BCI) applications is the occurrence of anomalous (unexpected) signals that might degrade BCI performance. This situation might slip the operator's attention since raw signals are not usually continuously visualized and monitored during BCI-actuated device operation. Anomalous data can f...

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 machine-learning aspects of BCI training. This work presents a novel co-adaptive training prot...

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 the end-users outside laboratory environments. To achieve this primary objective, it is necessary to augment a BCI with a series of components that allow end-users to type...

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 voluntarily and reliably stop the robot at any moment, not just drive from point to point. In this work...

We describe an approach to improving the design and de-velopment of Brain-Computer Interface (BCI) applications by simulating the error-prone characteristics and subjective feel of electroencephalogram (EEG), motor-imagery based BCIs. BCIs have the potential to enhance the quality of life of people who are severely disabled, but it is often time-co...

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 execution of motor imagery (MI) tasks. Successful self-paced interaction under this framework requir...

One important source of performance degradation in BCIs is bias towards one of the men-tal classes. Recent literature has focused on the general problem of classification accuracy drop, identifying non-stationarity as the generating factor, thus leading to several classi-fier adaptation approaches suggested as of today. In this work, we explicitly...

This paper discusses and evaluates the role of shared control approach in a BCI-based telepresence framework. Driving a mobile device by using human brain signals might improve the quality of life of people suffering from severely physical disabilities. By means of a bidirectional audio/video connection to a robot, the BCI user is able to interact...

Human activity recognition has been a major goal of research in the field of human -computer interaction. This paper pro-poses a method which employs a hierarchical structure of Hidden Markov Models (Layered HMMs) in an attempt to exploit inherent characteristics of human action for more ef-ficient recognition. The case study concerns actions of th...

This project aims at developing a biometric authentication system exploiting new features extracted by analysing the dynamic nature of various modalities, including motion analysis during ordinary tasks performed in front of a computer, analysis of speech, continous face and facial movement analysis, and even patterns for grasping objects. We test...