Imperial College London

Dr A. Aldo Faisal

Faculty of EngineeringDepartment of Bioengineering

Reader in Neurotechnology
 
 
 
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Contact

 

+44 (0)20 7594 6373a.faisal Website

 
 
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Assistant

 

Ms Antonia Szigeti +44 (0)20 7594 3148

 
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Location

 

4.08Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

142 results found

Liu Y, Gottesman O, Raghu A, Komorowski M, Faisal AA, Doshi-Velez F, Brunskill Eet al., 2018, Representation Balancing MDPs for Off-Policy Policy Evaluation, Thirty-second Annual Conference on Neural Information Processing Systems (NIPS)

We study the problem of off-policy policy evaluation (OPPE) in RL. In contrastto prior work, we consider how to estimate both the individual policy value and average policy value accurately. We draw inspiration from recent work in causal reasoning, and propose a new finite sample generalization error bound for value estimates from MDP models. Using this upper bound as an objective, we develop a learning algorithm of an MDP model with a balanced representation, and show that our approach can yield substantially lower MSE in a common synthetic domain and on a challenging real-world sepsis management problem.

CONFERENCE PAPER

Komorowski M, Celi L, Badawi O, Gordon AC, Faisal AAet al., 2018, The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care, NATURE MEDICINE, Vol: 24, Pages: 1716-+, ISSN: 1078-8956

JOURNAL ARTICLE

Ortega P, Colas C, Faisal AA, 2018, Compact Convolutional Neural Networks for Multi-Class, Personalised, Closed-Loop EEG-BCI, Pages: 136-141, ISSN: 2155-1774

© 2018 IEEE. For many people suffering from motor disabilities, assistive devices controlled with only brain activity are the only way to interact with their environment [1]. Natural tasks often require different kinds of interactions, involving different controllers the user should be able to select in a self-paced way. We developed a Brain-Computer Interface (BCI) allowing users to switch between four control modes in a self-paced way in real-time. Since the system is devised to be used in domestic environments in a user-friendly way, we selected non-invasive electroencephalographic (EEG) signals and convolutional neural networks (CNNs), known for their ability to find the optimal features in classification tasks. We tested our system using the Cybathlon BCI computer game, which embodies all the challenges inherent to real-time control. Our preliminary results show that an efficient architecture (SmallNet), with only one convolutional layer, can classify 4 mental activities chosen by the user. The BCI system is run and validated online. It is kept up-to-date through the use of newly collected signals along playing, reaching an online accuracy of 47.6% where most approaches only report results obtained offline. We found that models trained with data collected online better predicted the behaviour of the system in real-time. This suggests that similar (CNN based) offline classifying methods found in the literature might experience a drop in performance when applied online. Compared to our previous decoder of physiological signals relying on blinks, we increased by a factor 2 the amount of states among which the user can transit, bringing the opportunity for finer control of specific subtasks composing natural grasping in a self-paced way. Our results are comparable to those showed at the Cybathlon's BCI Race but further improvements on accuracy are required.

CONFERENCE PAPER

Woods B, Subramanian M, Shafti A, Faisal AAet al., 2018, Mecbanomyograpby Based Closed-Loop Functional Electrical Stimulation Cycling System, Pages: 179-184, ISSN: 2155-1774

© 2018 IEEE. Functional Electrical Stimulation (FES) systems are successful in restoring motor function and supporting paralyzed users. Commercially available FES products are open loop, meaning that the system is unable to adapt to changing conditions with the user and their muscles which results in muscle fatigue and poor stimulation protocols. This is because it is difficult to close the loop between stimulation and monitoring of muscle contraction using adaptive stimulation. FES causes electrical artefacts which make it challenging to monitor muscle contractions with traditional methods such as electromyography (EMG). We look to overcome this limitation by combining FES with novel mechanomyographic (MMG) sensors to be able to monitor muscle activity during stimulation in real time. To provide a meaningful task we built an FES cycling rig with a software interface that enabled us to perform adaptive recording and stimulation, and then combine this with sensors to record forces applied to the pedals using force sensitive resistors (FSRs); crank angle position using a magnetic incremental encoder and inputs from the user using switches and a potentiometer. We illustrated this with a closed-loop stimulation algorithm that used the inputs from the sensors to control the output of a programmable RehaStim 1 FES stimulator (Hasomed) in real-time. This recumbent bicycle rig was used as a testing platform for FES cycling. The algorithm was designed to respond to a change in requested speed (RPM) from the user and change the stimulation power (% of maximum current mA) until this speed was achieved and then maintain it.

CONFERENCE PAPER

Maymó MR, Shafti A, Faisal AA, 2018, Fast Orient: Lightweight Computer Vision for Wrist Control in Assistive Robotic Grasping

© 2018 IEEE. Wearable and Assistive robotics for human grasp support are broadly either tele-operated robotic arms or act through orthotic control of a paralyzed user's hand. Such devices require correct orientation for successful and efficient grasping. In many human-robot assistive settings, the end-user is required to explicitly control the many degrees of freedom making effective or efficient control problematic. Here we are demonstrating the off-loading of low-level control of assistive robotics and active orthotics, through automatic end-effector orientation control for grasping. This paper describes a compact algorithm implementing fast computer vision techniques to obtain the orientation of the target object to be grasped, by segmenting the images acquired with a camera positioned on top of the end-effector of the robotic device. The rotation needed that optimises grasping is directly computed from the object's orientation. The algorithm has been evaluated in 6 different scene backgrounds and end-effector approaches to 26 different objects. 94.8% of the objects were detected in all backgrounds. Grasping of the object was achieved in 91.1% of the cases and has been evaluated with a robot simulator confirming the performance of the algorithm.

WORKING PAPER

Cunningham J, Hapsari A, Guilleminot P, Shafti A, Faisal AAet al., 2018, The Supernumerary Robotic 3<sup>rd</sup> Thumb for Skilled Music Tasks

© 2018 IEEE. Wearable robotics bring the opportunity to augment human capability and performance, be it through prosthetics, exoskeletons, or supernumerary robotic limbs. The latter concept allows enhancing human performance and assisting them in daily tasks. An important research question is, however, whether the use of such devices can lead to their eventual cognitive embodiment, allowing the user to adapt to them and use them seamlessly as any other limb of their own. This paper describes the creation of a platform to investigate this. Our supernumerary robotic 3rd thumb was created to augment piano playing, allowing a pianist to press piano keys beyond their natural hand-span; thus leading to functional augmentation of their skills and the technical feasibility to play with 11 fingers. The robotic finger employs sensors, motors, and a human interfacing algorithm to control its movement in real-time. A proof of concept validation experiment has been conducted to show the effectiveness of the robotic finger in playing musical pieces on a grand piano, showing that naive users were able to use it for 11 finger play within a few hours.

WORKING PAPER

Lin C-H, Faisal AA, 2018, Decomposing sensorimotor variability changes in ageing and their connection to falls in older people, SCIENTIFIC REPORTS, Vol: 8, ISSN: 2045-2322

JOURNAL ARTICLE

Auepanwiriyakul C, Harston A, Orlov P, Shafti A, Faisal AAet al., 2018, Semantic Fovea: Real-time annotation of ego-centric videos with gaze context

© 2018 Copyright held by the owner/author(s). Visual context plays a crucial role in understanding human visual attention in natural, unconstrained tasks - the objects we look at during everyday tasks provide an indicator of our ongoing attention. Collection, interpretation, and study of visual behaviour in unconstrained environments therefore is necessary, however presents many challenges, requiring painstaking hand-coding. Here we demonstrate a proof-of-concept system that enables real-time annotation of objects in an egocentric video stream from head-mounted eye-tracking glasses. We concurrently obtain a live stream of user gaze vectors with respect to their own visual field. Even during dynamic, fast-paced interactions, our system was able to recognise all objects in the user’s field-of-view with moderate accuracy. To validate our concept, our system was used to annotate an in-lab breakfast scenario in real time.

CONFERENCE PAPER

Orlov P, Shafti A, Auepanwiriyakul C, Songur N, Faisal AAet al., 2018, A Gaze-contingent Intention Decoding Engine for human augmentation

© 2018 Copyright held by the owner/author(s). Humans process high volumes of visual information to perform everyday tasks. In a reaching task, the brain estimates the distance and position of the object of interest, to reach for it. Having a grasp intention in mind, human eye-movements produce specific relevant patterns. Our Gaze-Contingent Intention Decoding Engine uses eye-movement data and gaze-point position to indicate the hidden intention. We detect the object of interest using deep convolution neural networks and estimate its position in a physical space using 3D gaze vectors. Then we trigger the possible actions from an action grammar database to perform an assistive movement of the robotic arm, improving action performance in physically disabled people. This document is a short report to accompany the Gaze-contingent Intention Decoding Engine demonstrator, providing details of the setup used and results obtained.

CONFERENCE PAPER

Parbhoo S, Gottesman O, Ross AS, Komorowski M, Faisal A, Bon I, Roth V, Doshi-Velez Fet al., 2018, Improving counterfactual reasoning with kernelised dynamic mixing models., PLoS One, Vol: 13

Simulation-based approaches to disease progression allow us to make counterfactual predictions about the effects of an untried series of treatment choices. However, building accurate simulators of disease progression is challenging, limiting the utility of these approaches for real world treatment planning. In this work, we present a novel simulation-based reinforcement learning approach that mixes between models and kernel-based approaches to make its forward predictions. On two real world tasks, managing sepsis and treating HIV, we demonstrate that our approach both learns state-of-the-art treatment policies and can make accurate forward predictions about the effects of treatments on unseen patients.

JOURNAL ARTICLE

Xiloyannis M, Gavriel C, Thomik AAC, Faisal AAet al., 2017, Gaussian Process Autoregression for Simultaneous Proportional Multi-Modal Prosthetic Control With Natural Hand Kinematics, IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, Vol: 25, Pages: 1785-1801, ISSN: 1534-4320

JOURNAL ARTICLE

Kotti M, Duffell LD, Faisal AA, McGregor AHet al., 2017, Detecting knee osteoarthritis and its discriminating parameters using random forests, MEDICAL ENGINEERING & PHYSICS, Vol: 43, Pages: 19-29, ISSN: 1350-4533

JOURNAL ARTICLE

Maimon-Dror RO, Fernandez-Quesada J, Zito GA, Konnaris C, Dziemian S, Faisal AAet al., 2017, Towards free 3D end-point control for robotic-assisted human reaching using binocular eye tracking, International Conference on Rehabilitation Robotics (ICORR), Publisher: IEEE, Pages: 1049-1054, ISSN: 1945-7898

CONFERENCE PAPER

Noronha B, Dziemian S, Zito GA, Konnaris C, Faisal AAet al., 2017, "Wink to grasp" - comparing Eye, Voice & EMG gesture control of grasp with soft-robotic gloves, International Conference on Rehabilitation Robotics (ICORR), Publisher: IEEE, Pages: 1043-1048, ISSN: 1945-7898

CONFERENCE PAPER

Pedotti A, Azevedo L, Faisal A, 2017, Foreword, Pages: VII-VIII

CONFERENCE PAPER

Makin TR, de Vignemont F, Faisal AA, 2017, Neurocognitive barriers to the embodiment of technology, NATURE BIOMEDICAL ENGINEERING, Vol: 1, ISSN: 2157-846X

JOURNAL ARTICLE

Faisal AA, Neishabouri A, 2016, Fundamental Constraints on the Evolution of Neurons, The Wiley-Blackwell Handbook of Evolutionary Neuroscience, Pages: 153-172, ISBN: 9781118316757

© 2017 John Wiley & Sons, Ltd. All rights reserved. This chapter focuses on two fundamental constraints that apply to any form of information processing system, be it a cell, a brain or a computer: Noise (random variability) and Energy (metabolic demand). It shows how these two constraints are fundamentally limited by the basic biophysical properties of the brain's building blocks (protein, fats, and salty water) and link nervous system structure to function. The understanding of the interdependence of information and energy has profoundly influenced the development of efficient telecommunication systems and computers. Noise diminishes the capacity to receive, process, and direct information, the key tasks of the brain. Investing in the brain's design can reduce the effects of noise, but this investment often increases energetic requirements, which is likely to be evolutionary unfavourable. The stochasticity of the system becomes critical when its inherent randomness makes it operationally infeasible, that is, when random action potential (APs) become as common as evoked APs.

BOOK CHAPTER

Corrales-Carvajal VM, Faisal AA, Ribeiro C, 2016, Internal states drive nutrient homeostasis by modulating exploration-exploitation trade-off., Elife, Vol: 5

Internal states can profoundly alter the behavior of animals. A quantitative understanding of the behavioral changes upon metabolic challenges is key to a mechanistic dissection of how animals maintain nutritional homeostasis. We used an automated video tracking setup to characterize how amino acid and reproductive states interact to shape exploitation and exploration decisions taken by adult Drosophila melanogaster. We find that these two states have specific effects on the decisions to stop at and leave proteinaceous food patches. Furthermore, the internal nutrient state defines the exploration-exploitation trade-off: nutrient-deprived flies focus on specific patches while satiated flies explore more globally. Finally, we show that olfaction mediates the efficient recognition of yeast as an appropriate protein source in mated females and that octopamine is specifically required to mediate homeostatic postmating responses without affecting internal nutrient sensing. Internal states therefore modulate specific aspects of exploitation and exploration to change nutrient selection.

JOURNAL ARTICLE

Corrales-Carvajal VM, Faisal AA, Ribeiro C, 2016, Internal states drive nutrient homeostatis by modulating exploration-exploitation trade-off, ELIFE, Vol: 5, ISSN: 2050-084X

JOURNAL ARTICLE

Bergsma A, Lobo-Prat J, Vroom E, Furlong P, Herder JL, Workshop Participantset al., 2016, 1st Workshop on Upper-Extremity Assistive Technology for People with Duchenne: State of the art, emerging avenues, and challenges: April 27th 2015, London, United Kingdom., Neuromuscul Disord, Vol: 26, Pages: 386-393

JOURNAL ARTICLE

Lorenz R, Monti RP, Violante IR, Anagnostopoulos C, Faisal AA, Montana G, Leech Ret al., 2016, The Automatic Neuroscientist: A framework for optimizing experimental design with closed-loop real-time fMRI, NEUROIMAGE, Vol: 129, Pages: 320-334, ISSN: 1053-8119

JOURNAL ARTICLE

Faisal AA, 2016, Action Grammars - Extraction, recognition and prediction of movement primitives in tool-making, 85th Annual Meeting of the American-Association-of-Physical-Anthropologists, Publisher: WILEY-BLACKWELL, Pages: 141-141, ISSN: 0002-9483

CONFERENCE PAPER

Konnaris C, Thomik AAC, Faisal AA, 2016, Sparse Eigenmotions Derived from Daily Life Kinematics Implemented on a Dextrous Robotic Hand, 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob), Publisher: IEEE, Pages: 1358-1363, ISSN: 2155-1782

CONFERENCE PAPER

Behbahani FMP, Singla-Buxarrais G, Faisal AA, 2016, Haptic SLAM: An Ideal Observer Model for Bayesian Inference of Object Shape and Hand Pose from Contact Dynamics, 10th International Conference on Haptics - Perception, Devices, Control, and Applications (EuroHaptics), Publisher: SPRINGER INT PUBLISHING AG, Pages: 146-157, ISSN: 0302-9743

CONFERENCE PAPER

Tostado PM, Abbott WW, Faisal AA, 2016, 3D gaze cursor: continuous calibration and end-point grasp control of robotic actuators, IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE, Pages: 3295-3300, ISSN: 1050-4729

CONFERENCE PAPER

Rodriguez M, Sylaidi A, Faisal AA, 2016, An fMRI-Compatible System for 3DOF Motion Tracking of Objects in Haptic Motor Control Studies, Editors: Londral, Encarnacao, Publisher: SPRINGER INT PUBLISHING AG, Pages: 115-123, ISBN: 978-3-319-26240-6

BOOK CHAPTER

Lourenco PR, Abbott WW, Faisal AA, 2016, Supervised EEG Ocular Artefact Correction Through Eye-Tracking, Editors: Londral, Encarnacao, Publisher: SPRINGER INT PUBLISHING AG, Pages: 99-113, ISBN: 978-3-319-26240-6

BOOK CHAPTER

Konnaris C, Gavriel C, Thomik AAC, Faisal AAet al., 2016, EthoHand: A Dexterous Robotic Hand with Ball-Joint Thumb Enables Complex In-hand Object Manipulation, 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob), Publisher: IEEE, Pages: 1154-1159, ISSN: 2155-1782

CONFERENCE PAPER

Lorenz R, Monti RP, Hampshire A, Koush Y, Anagnostopoulos C, Faisal AA, Sharp D, Montana G, Leech R, Violante IRet al., 2016, Towards tailoring non-invasive brain stimulation using real-time fMRI and Bayesian optimization, 6th International Workshop on Pattern Recognition in Neuroimaging (PRNI), Publisher: IEEE, Pages: 49-52, ISSN: 2330-9989

CONFERENCE PAPER

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