Biology subjects

Faisal, A.

Publications and source records attributed to Faisal, A..

5 recordsLinked to original sources

bioRxiv2018

Reconstructing meaning from bits of information

We can easily identify a dog merely by the sound of barking or an orange by its citrus scent. In this work, we study the neural underpinnings of how the brain combines bits of information into meaningful object representations. Modern theories of semantics posit that the meaning of words can be decomposed into a unique combination of individual semantic features (e.g., \"barks\", \"has citrus scent\"). Here, participants received clues of individual objects in form of three isolated semantic features, given as verbal descriptions. We used machine-learning-based neural decoding to learn a mapping between individual semantic features and BOLD activation patterns. We discovered that the recorded brain patterns were best decoded using a combination of not only the three semantic features that were presented as clues, but a far richer set of semantic features typically linked to the target object. We conclude that our experimental protocol allowed us to observe how fragmented information is combined into a complete semantic representation of an object and suggest neuroanatomical underpinnings for this process.

neuroscience↗

bioRxiv2018

Cracking the problem of neural representations of abstract words: grounding word meanings in language itself

In order to describe how humans represent meaning in the brain, one must be able to account for not just concrete words but, critically, also abstract words which lack a physical referent. Hebbian formalism and optimization are basic principles of brain function, and they provide an appealing approach for modeling word meanings based on word co-occurrences. Here, we built a model of the semantic space based on word statistics in a large text corpus, which was able to decode items from brain signals. In the model, word abstractness emerged from the statistical regularities of the language environment. This salient property of the model co-varied, at 280-420 ms after word presentation, with activity in the left-hemisphere frontal, anterior temporal and superior parietal cortex that have been linked with processing of abstract words. In light of these results, we propose that the neural encoding of word meanings is importantly grounded in language through statistical regularities.

neuroscience↗

bioRxiv2018

Deep Learning personalised, closed-loop Brain-Computer Interfaces for multi-way classification

Exoskeletons and robotic devices are for many motor disabled people the only way to interact with their envi-ronment. Our lab previously developed a gaze guided assistive robotic system for grasping. It is well known that the same natural task can require different interactions described by different dynamical systems that would require different robotic controllers and their selection by the user in a self paced way. Therefore, we investigated different ways to achieve transitions between multiple states, finding that eye blinks were the most reliable to transition from off to control modes (binary classification) compared to voice and electromyography. In this paper be expanded on this work by investigating brain signals as sources for control mode switching. We developed a Brain Computer Interface (BCI) that allows users to switch between four control modes in 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 (ConvNets), known by their capability to find the optimal features for a classification task, which we hypothesised would add flexibility to the system in terms of which mental activities the user could perform to control it. We tested our system using the Cybathlon BrainRunners computer game, which represents all the challenges inherent to real time control. Our preliminary results show that an efficient architecture (SmallNet) composed by a convolutional layer, a fully connected layer and a sigmoid classification layer, is able to classify 4 mental activities that the user chose to perform. For his preferred mental activities, we run and validated the system online and retrained the system using online collected EEG data. We achieved 47, 6% accuracy in online operation in the 4-way classification task. In particular we found that models trained with online collected data predicted better the behaviour of the system in real time suggesting, as a side note, that similar (ConvNets based) offline classifying methods present in literature might find a decay in performance when applied online. To the best of our knowledge this is the first time such an architecture is tested in an online operation task. While compared to our previous method relying on blinks with this one we reduced in less than half (1.6 times) the accuracy but increased by 2 the amount of states among which we can transit, bringing the opportunity for finer control of specific subtasks composing natural grasping in a self paced way.

bioengineering↗

bioRxiv2018

Natural Gaze Data Driven Wheelchair

Natural eye movements during navigation have long been considered to reflect planning processes and link to users future action intention. We investigate here whether natural eye movements during joystick-based navigation of wheel-chairs follow identifiable patterns that are predictive of joystick actions. To place eye movements in context with driving intentions, we combine our eye tracking with a 3D depth camera system, which allows us to identify which eye movements have the floor as gaze target and distinguish them from other non-navigation related eye movements. We find consistent patterns of eye movements on the floor predictive of steering commands issued by the driver in all subjects. Based on this empirical data we developed two gaze decoders using supervised machine learning techniques and enabled each of these drivers to then steer the wheelchair by imagining they were using a joystick to trigger appropriate natural eye movements via motor imagery. We show that all subjects are able to navigate their wheelchair \"by eye\" learning it within a short time span of minutes. Our work shows that simple gaze-based decoding without need for artificial user interfaces suffices to restore mobility and increasing participation in daily life.

bioengineering↗

bioRxiv2017

The role of sensorimotor variability and computation in falls in the elderly

The relationship between sensorimotor variability and falls in elderly has not been well investigated. We designed and used a motor task having shared biomechanics of walking and obstacle negotiation to quantify sensorimotor variability related to locomotion across age. We also applied sensory psychophysics to pinpoint specific sensory systems associated with sensorimotor variability. We found that sensorimotor variability in foot placement increases continuously with age. We further showed that increased sensory variability, specifically increased proprioceptive variability, the vital cause of more variable foot placement in the elderly. Notably, elderly participants relied more on the vision to judge their own foots height compared to the young, suggesting a shift in multisensory integration strategy to compensate for degenerated proprioception. We further modelled the probability of tripping-over based on the relationship between sensorimotor variability and age and found a good correspondence between model prediction and community-based data. We revealed increased sensorimotor variability, modulated by sensation precision, a potentially vital mechanism of raised tripping-over and thus fall events in the elderly. Therefore, our tasks, which quantify sensorimotor variability, can be used for trip-over probability assessment and, with adjustments, potentially applied as a training program to mitigate trip-over risk.

neuroscience↗