| 1. |
- Carmegren, E., et al.
(författare)
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Dependability Evaluation of an Online Pupillometry-based Feedback System for Optimized Training
- 2022
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Ingår i: 2022 45th Jubilee International Convention on Information, Communication and Electronic Technology, MIPRO 2022 - Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9789532331035 ; , s. 327-332
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Konferensbidrag (refereegranskat)abstract
- The online pupillometry-based feedback system is intended as a cognitive training and rehabilitation system developed at Mälardalen University. The purpose of the system is to engage a person in a cognitive computer task whose difficulty is adjusted in real time depending on the person's cognitive load. Previous research has uncovered a significant correlation between cognitive load and pupil dilation, suggesting that electroencephalogram usage for estimating cognitive load can be eliminated. The online pupillometry-based feedback system is measuring the pupil-diameter in real time to classify cognitive load using a neural network. The classification of cognitive load is used to modulate the difficulty level of the cognitive task, with the purpose of challenging the participant and to optimize the cognitive training. At the current state the system is fully integrated, but possesses no fault-tolerant features to produce a long-term reliable service. This paper proposes a fault-tolerant architecture for the online pupillometry-based feedback system, for which internal repairs and failure rates are modeled using continuous-time Markov chains. The results show adequacy of the extended architecture, assuming slightly optimistic failure rates. Even though the system is specific, the reliability approach presented can be applied on other medical devices and systems.
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| 2. |
- Di Bello, F., et al.
(författare)
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Prefrontal Control of Proactive and Reactive Mechanisms of Visual Suppression
- 2022
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Ingår i: Cerebral Cortex. - : Oxford University Press. - 1047-3211 .- 1460-2199. ; 32:13, s. 2745-2761
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Tidskriftsartikel (refereegranskat)abstract
- In everyday life, we are continuously struggling at focusing on our current goals while at the same time avoiding distractions. Attention is the neuro-cognitive process devoted to the selection of behaviorally relevant sensory information while at the same time preventing distraction by irrelevant information. Distraction can be prevented proactively, by strategically prioritizing task-relevant information at the expense of irrelevant information, or reactively, by suppressing the ongoing processing of distractors. The distinctive neuronal signature of these suppressive mechanisms is still largely unknown. Thanks to machine-learning decoding methods applied to prefrontal cortical activity, we monitor the dynamic spatial attention with an unprecedented spatial and temporal resolution. We first identify independent behavioral and neuronal signatures for long-term (learning-based spatial prioritization) and short-term (dynamic spatial attention) mechanisms. We then identify distinct behavioral and neuronal signatures for proactive and reactive suppression mechanisms. We find that while distracting task-relevant information is suppressed proactively, task-irrelevant information is suppressed reactively. Critically, we show that distractor suppression, whether proactive or reactive, strongly depends on the implementation of both long-term and short-term mechanisms of selection. Overall, we provide a unified neuro-cognitive framework describing how the prefrontal cortex deals with distractors in order to flexibly optimize behavior in dynamic environments.
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| 3. |
- Farbod Kia, Saba, et al.
(författare)
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Readout of the intrinsic and extrinsic properties of a stimulus from un-experienced neuronal activities : towards cognitive neuroprostheses
- 2011
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Ingår i: Journal of Physiology - Paris. - France : Elsevier BV. - 0928-4257 .- 1769-7115. ; 105:1-3, s. 115-122
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Tidskriftsartikel (refereegranskat)abstract
- While sensory and motor systems have attracted most of the research effort in the field neuroprosthetics, little attention has been devoted to higher order cortical processes. Here, we propose a first step in the direction of applying neural decoding to the study and manipulation of visuospatial attention, an endogenous process at the interface between sensory and motor functions. To this aim, we investigate whether the offline activity of apopulation of non-human primate frontal eye field neurons (FEF) in response to an endogenous cue can be readout on a trial by trial basis to providea precise description of the cue's attributes, namely, its location and identity, but also the allocation of attention following its interpretation. Using alinear decoder, we reach up to 86% correct predictions for the different decoded variables, including the spatial allocation of endogenous attention. We show that the decoding performance drops on incorrect trials, indicating that cue encoding participates to the animal's behavioral performance. Last, we show that the temporal resolution of the decoding influences readout performance. These results are a strong indication of the feasibility of the readout of endogenous variables by standard decoding algorithms, on a suboptimal dataset. However, its validity remains to be proved in a real-time situation.
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| 4. |
- Jaen Ortega, A. A., et al.
(författare)
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On Understanding the Role of Exoskeleton Robots in Hand Rehabilitation : A Brief Review
- 2022
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Ingår i: Proceedings - 2022 8th International Engineering, Sciences and Technology Conference, IESTEC 2022. - : Institute of Electrical and Electronics Engineers Inc.. - 9781665498432 ; , s. 432-439
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Konferensbidrag (refereegranskat)abstract
- Hand rehabilitation has been widely studied since it affects the life quality and independence of those affected. Hand impairment can be caused by several conditions, among them strokes and other cerebrovascular accidents, affecting the capabilities of those who survive them in performing the activities of daily living (ADL). Rehabilitation seeks to restore the ability of a person to perform these crucial ADL. There is a current trend in using robotic rehabilitation and other industry 4.0 tools since it can provide a safe, intensive, and task-oriented at a relatively low cost, which can be combined with other technologies such as virtual and augmented reality, BCI, haptics, and others. Moreover, it can provide accessibility in the face of current panoramas such as COVID-19. Hand exoskeleton robots are one of the most extended robotic devices for rehabilitation. However, a design adapted to the patient's needs is necessary to achieve their capability fully and succeed in rehabilitation. One of the main challenges is that several considerations and parameters affect these devices' design and the broad approaches that can be followed. This brief review aims to understand and empathize as a source of inspiration during the design process of hand exoskeleton robots for rehabilitation.
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| 5. |
- Leon, Miguel, et al.
(författare)
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Feature Selection of EEG Oscillatory Activity Related to Motor Imagery Using a Hierarchical Genetic Algorithm
- 2019
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Ingår i: 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728121536 ; , s. 87-94
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Konferensbidrag (refereegranskat)abstract
- Motor Imagery (MI) classification from neural activity is thought to represent valuable information that can be provided as real-time feedback during rehabilitation after for example a stroke. Previous studies have suggested that MI induces partly subject-specific EEG activation patterns, suggesting that individualized classification models should be created. However, due to fatigue of the user, only a limited number of samples can be recorded and, for EEG recordings, each sample is often composed of a large number of features. This combination leads to an undesirable input data set for classification. In order to overcome this constraint, we propose a new methodology to create and select features from the EEG signal in two steps. First, the input data is divided into different windows to reduce the cardinality of the input. Secondly, a Hierarchical Genetic Algorithm is used to select relevant features using a novel fitness function which combines the data reduction with a correlation feature selection measure. The methodology has been tested on EEG oscillatory activity recorded from 6 healthy volunteers while they performed an MI task. Results have successfully proven that a classification above 75% can be obtained in a restrictive amount of time (0.02 s), reducing the number of features by almost 90%.
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| 6. |
- Petterson, Hannes Nitz, et al.
(författare)
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Iris identification using wavelet decomposition and gabor filter
- 2020
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Ingår i: 2020 43rd International Convention on Information, Communication and Electronic Technology, MIPRO 2020 - Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9789532330991 ; , s. 265-270
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Konferensbidrag (refereegranskat)abstract
- Biometric authentication has seen a widespread increase in popularity as supporting technology has become common in mass produced consumer electronics. Like fingerprints, each individual has unique patterns in the iris, which makes it a common approach for implementing visual biometric authentication. The paper describes a novel system for extracting the iris pattern and using it for identification of people. The system uses Haar wavelet decomposition and 2D Gabor filtering to extract the pattern data. The pattern data is then used with bitwise XOR comparison for final identification matching. Instead of manually selecting parameters for the Gabor filter, a machine learning method called Particle Swarm Optimization was used. The parameters that gave the best matching result were then implemented in the filter design. The implemented system was evaluated on images obtained from 6 individuals in different settings. The evaluation showed that matching identification could be achieved for the database used. The prepossessing of images with Independent Component Analysis was also used to remove the reflections on the images but that did not improve the classification significantly. Still we were able to perfectly distinguish between the individuals. Further preprocessing and a larger training database would be required to get more general and robust results.
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| 7. |
- Syrjänen, Elmeri, et al.
(författare)
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Individual temporal and spatial dynamics of learning to control central Beta activity in neurofeedback training
- 2023
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Ingår i: 2023 11th International IEEE/EMBS Conference on Neural Engineering (NER). - : IEEE. - 9781665462921
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Konferensbidrag (refereegranskat)abstract
- Neurofeedback (NFB) and Brain-Computer Interface (BCI) research seldom present within-session individual learning dynamics. This is even though a large proportion of NFB and BCI users cannot learn neural self-regulation required to control the feedback. Understanding the time course and learning variability between participants might allow us to design better NFB and BCI protocols to promote learning of neural self-regulation. The importance of developing novel NFB and BCI protocols becomes apparent, considering the clinical utility of these techniques. Tuning the brain to perform optimally could provide for long-term non-pharmacological treatment without any drug-associated side effects. This paper reports the strategies used by participants and the individual dynamics of central Beta NFB downregulation training and associated mental strategies for nine participants. The results showed that all participants could learn to downregulate their central Beta power in a single session, however, the dynamics of learning differed between participants. We visually identified two learning dynamics; 1) a continual decrease in Beta power and 2) an initial decrease followed by a stable level of Beta power. Topographic plots indicated high spatial variability in Beta power decreases in participants. Responses from end-of-session debriefing indicated that all participants felt they could control the feedback. Although participants could control the feedback, an optimal mental strategy for controlling central Beta power was not revealed.
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| 8. |
- Tidare, Jonatan, et al.
(författare)
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Discriminating EEG spectral power related to mental imagery of closing and opening of hand
- 2019
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Ingår i: 2019 9TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER). - : IEEE. - 9781538679210 ; , s. 307-310
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Konferensbidrag (refereegranskat)abstract
- ElectroEncephaloGram (EEG) spectral power has been extensively used to classify Mental Imagery (MI) of movements involving different body parts. However, there is an increasing need to enable classification of MI of movements within the same limb. In this work, EEG spectral power was recorded in seven subjects while they performed MI of closing (grip) and opening (extension of fingers) the hand. The EEG data was analyzed and the feasibility of classifying MI of the two movements were investigated using two different classification algorithms, a linear regression and a Convolutional Neural Network (CNN). Results show that only the CNN is able to significantly classify MI of opening and closing of the hand with an average classification accuracy of 60.4%. This indicates the presence of higher-order non-linear discriminatory information and demonstrates the potential of using CNN in classifying MI of same-limb movements.
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| 9. |
- Tidare, Jonatan, et al.
(författare)
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Evaluation of closed-loop feedback system delay a time-critical perspective for neurofeedback training
- 2018
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Ingår i: BIODEVICES 2018 - 11th International Conference on Biomedical Electronics and Devices, Proceedings; Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018. - : SciTePress. - 9789897582776 ; , s. 187-193
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Konferensbidrag (refereegranskat)abstract
- Neurofeedback in real-time has proven effective when subjects learn to control a BCI. To facilitate learning, a closed-loop feedback system should provide neurofeedback with maximal accuracy and minimal delay. In this article, we propose a modular system for real-time neurofeedback experiments and evaluate its performance as a function of increased stress level applied to the system. The system shows stable behavior and decent performance when streaming with many EEG channels (36-72) and 500-5000 Hz, which is common in BCI setups. With very low data loads (1 channel, 500-1000 Hz) the performance dropped significantly and the system became highly unpredictable. We show that the system delays did not correlate linearly with the stress-level applied to the system, emphasizing the importance of system delay tests before conducting real-time BCI-experiments.
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| 10. |
- Tidare, Jonatan
(författare)
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Temporal representation of Motor Imagery : towards improved Brain-Computer Interface-based strokerehabilitation
- 2021
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Practicing Motor Imagery (MI) with a Brain-Computer Interface (BCI) has shown promise in promoting motor recovery in stroke patients. A BCI records a person’s brain activity and provides feedback to the person in real time, which allows the person to practice his or her brain activity. By imagining a movement (performing MI) such as gripping with their hand, cortical areas in the brain are activated that largely overlaps with those activated during the actual hand movement. A BCI can provide positive feedback when the hand-related cortical areas are activated during MI, which helps a person to learn how to perform MI. Despite evidence that stroke patients may recover some motor function from practicing MI with BCI feedback thanks to the feedback provided from a BCI, the effectiveness and reliability of BCI-based rehabilitation are still poor. A BCI can detect MI by analyzing patterns of features from the brain activity. The most common features are extracted from the oscillatory activity in the brain. In BCI research, MI is often treated as a static pattern of features, which is detected by using machine learning algorithms to assign activity into a binary state. However, this model of MI may be inaccurate. Analyzing brain activity as dynamically varying over time and with a continuous measure of strength could better represent the cortical activity related to MI. In this Licentiate thesis, I explore a method for analyzing the temporal dynamic of MI-activity with a continuous measure of strength. Brain activity was recorded with electroencephalography (EEG) and subject-specific feature patterns were extracted from a group of healthy subjects while they performed MI of two opposing hand movements: opening and closing the hand. Although MI of the two same-hand movements could not be discriminated, the continuous output from a machine learning algorithm was shown to correlate well with MI-related feature patterns. The temporal analysis also revealed that MI is dynamically encoded early, but later stabilizes into a more static pattern of brain activity. Last, to accommodate for higher temporal resolution of MI, I designed and evaluated a BCI framework by its feedback delay and uncertainty as a function of the stress on the system and found a non-linear correlation. These results could be essential for developing a BCI with time-critical feedback.To summarize, in this Licentiate thesis I propose a promising method for analyzing and extracting a temporal representation of MI, enabling relevant and continuous neurofeedback which may contribute to clinical advances in BCI-based stroke rehabilitation.
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| 11. |
- Tidare, Jonatan, et al.
(författare)
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Time-resolved estimation of strength of Motor Imagery representation by multivariate EEG decoding.
- 2020
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Ingår i: Journal of Neural Engineering. - : IOP Publishing. - 1741-2560 .- 1741-2552. ; 18
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE: Multivariate decoding enables access to information encoded in multiple brain activity features with high temporal resolution. However, whether the strength, of which this information is represented in the brain, can be extracted across time within single trials remains largely unexplored.APPROACH: In this study, we addressed this question by applying a Support Vector Machine (SVM) to extract Motor Imagery (MI) representations, from Electroencephalogram (EEG) data, and by performing time-resolved single-trial analyses of the multivariate decoding. EEG was recorded from a group of healthy participants during MI of opening and closing of the same hand.MAIN RESULTS: Cross-temporal decoding revealed both dynamic and stationary MI-relevant features during the task. Specifically, features representing MI evolved dynamically early in the trial and later stabilized into a stationary network of MI features. Using a Hierarchical Genetic Algorithm (HGA) for selection of MI-relevant features, we identified primarily contralateral alpha and beta frequency features over the sensorimotor and parieto-occipital cortices as stationary which extended into a bilateral pattern in the later part of the trial. During the stationary encoding of MI, by extracting the SVM prediction scores, we analyzed MI-relevant EEG activity patterns with respect to the temporal dynamics within single trials. We show that the SVM prediction score correlates to the amplitude of univariate MI-relevant features (as documented from an extensive repertoire of previous MI studies) within single trials, strongly suggesting that these are functional variations of MI strength hidden in trial averages.SIGNIFICANCE: Our work demonstrates a powerful approach for estimating MI strength continually within single trials, having far-reaching impact for single-trial analyses. In terms of MI neurofeedback for motor rehabilitation, these results set the ground for more refined neurofeedback reflecting the strength of MI that can be provided to patients continually in time.
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| 12. |
- Åstrand, Elaine
(författare)
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A continuous time-resolved measure decoded from EEG oscillatory activity predicts working memory task performance
- 2018
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Ingår i: Journal of Neural Engineering. - : IOP PUBLISHING LTD. - 1741-2560 .- 1741-2552. ; 15:3
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Tidskriftsartikel (refereegranskat)abstract
- Objective. Working memory (WM), crucial for successful behavioral performance in most of our everyday activities, holds a central role in goal-directed behavior. As task demands increase, inducing higher WM load, maintaining successful behavioral performance requires the brain to work at the higher end of its capacity. Because it is depending on both external and internal factors, individual WM load likely varies in a continuous fashion. The feasibility to extract such a continuous measure in time that correlates to behavioral performance during a working memory task remains unsolved. Approach. Multivariate pattern decoding was used to test whether a decoder constructed from two discrete levels of WM load can generalize to produce a continuous measure that predicts task performance. Specifically, a linear regression with L2-regularization was chosen with input features from EEG oscillatory activity recorded from healthy participants while performing the n-back task, n is an element of [1, 2]. Main results. The feasibility to extract a continuous time-resolved measure that correlates positively to trial-bytrial working memory task performance is demonstrated (r = 0.47, p < 0.05). It is furthermore shown that this measure allows to predict task performance before action (r = 0.49, p < 0.05). We show that the extracted continuous measure enables to study the temporal dynamics of the complex activation pattern of WM encoding during the n-back task. Specifically, temporally precise contributions of different spectral features are observed which extends previous findings of traditional univariate approaches. Significance. These results constitute an important contribution towards a wide range of applications in the field of cognitive brain-machine interfaces. Monitoring mental processes related to attention and WM load to reduce the risk of committing errors in high-risk environments could potentially prevent many devastating consequences or using the continuous measure as neurofeedback opens up new possibilities to develop novel rehabilitation techniques for individuals with degraded WM capacity.
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| 13. |
- Åstrand, Elaine, et al.
(författare)
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Comparison of Classifiers for Decoding Sensory and Cognitive Information from Prefrontal Neuronal Populations.
- 2014
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 9:1, s. 1-14
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Tidskriftsartikel (refereegranskat)abstract
- Decoding neuronal information is important in neuroscience, both as a basic means to understand how neuronal activity is related to cerebral function and as a processing stage in driving neuroprosthetic effectors. Here, we compare the readout performance of six commonly usedclassifiers at decoding two different variables encoded by the spiking activity of the non-human primate frontal eye fields (FEF): the spatial position of a visual cue, and the instructed orientation of the animal's attention. While the first variable is exogenously driven by the environment, the second variable corresponds to the interpretation of the instruction conveyed by the cue; it is endogenously driven and corresponds to the output of internalcognitive operations performed on the visual attributes of the cue. These two variables were decoded using either a regularized optimal linear estimator in its explicit formulation, an optimal linear artificial neural network estimator, a non-linear artificial neural network estimator, a non-linear naïve Bayesian estimator, a non-linear Reservoir recurrent network classifier or a non-linear Support Vector Machine classifier. Our results suggest that endogenous information such as the orientation of attention can be decoded from the FEF with the same accuracy as exogenous visualinformation. All classifiers did not behave equally in the face of population size and heterogeneity, the available training and testing trials, the subject's behavior and the temporal structure of the variable of interest. In most situations, the regularized optimal linear estimator and the non-linear Support Vector Machine classifiers outperformed the other tested decoders.
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| 14. |
- Åstrand, Elaine, et al.
(författare)
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Differential dynamics of spatial attention, position, and color coding within the parietofrontal network
- 2015
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Ingår i: Journal of Neuroscience. - United States. - 0270-6474 .- 1529-2401. ; 35:7, s. 3174-3189
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Tidskriftsartikel (refereegranskat)abstract
- Despite an ever growing knowledge on howparietal and prefrontal neurons encode low-level spatial and color information or higher-level information, such as spatial attention, an understanding of how these cortical regions process neuronal information at the population level is still missing. A simple assumption would be that the function and temporal response profiles of these neuronal populations match that of its constituting individual cells. However, several recent studies suggest that this is not necessarily the case and that the single-cell approach overlooks dynamic changes in how information is distributed over the neuronal population. Here, we use a time-resolved population pattern analysis to explore how spatialposition, spatial attention and color information are differentially encoded and maintained in the macaque monkey prefrontal (frontal eye fields) and parietal cortex (lateral intraparietal area). Overall, our work brings about three novel observations. First, we show that parietal and prefrontal populations operate in two distinct population regimens for the encoding of sensory and cognitive information: a stationary mode and a dynamic mode. Second, we show that the temporal dynamics of a heterogeneous neuronal population brings about complementary information to that of its functional subpopulations. Thus, both need to be investigated in parallel. Last, we show that identifying the neuronal configuration in which a neuronal population encodes given information can serve to reveal this same information in a different context. All together, this work challenges common views on neural coding in the parietofrontal network.
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| 15. |
- Åstrand, Elaine, et al.
(författare)
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Direct Two-Dimensional Access to the Spatial Location of Covert Attention in Macaque Prefrontal Cortex.
- 2016
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Ingår i: Current Biology. - : Elsevier BV. - 0960-9822 .- 1879-0445. ; 26:13, s. 1699-1704
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Tidskriftsartikel (refereegranskat)abstract
- Direct access to motor cortical information now enables tetraplegic patients to precisely control neuroprostheses and recover some autonomy. Incontrast, explicit access to higher cortical cognitive functions, such as covert attention, has been missing. Indeed, this cognitive information, known only to the subject, can solely be inferred by an observer from the subject's overt behavior. Here, we present direct two-dimensional real-time access to where monkeys are covertly paying attention, using machine-learning decoding methods applied to their ongoing prefrontal cortical activity. Decoded attention was highly predictive of overt behavior in a cued target-detection task. Indeed, monkeys had a higher probability of detecting a visual stimulus as the distance between decoded attention and stimulus location decreased. This was true whether the visual stimulus was presented at the cued target location or at another distractor location. In error trials, in which the animals failed to detect the cued target stimulus, both the locations of attention and visual cue were misencoded. This misencoding coincided with a specific state of the prefrontal cortical population in which the shared variability between its different neurons (or noise correlations) was high, even before trial onset. This observation strongly suggests a functional link between high noise-correlation states and attentional failure. Overall, this real-time access to the attentional spotlight, as well as the identification of a neural signature of attentional lapses, open new perspectives both to the study of the neural bases of attention and to the remediation or enhancement of the attentional function using neurofeedback.
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| 16. |
- Åstrand, Elaine, et al.
(författare)
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EEG non-stationarity across multiple sessions during a Motor Imagery-BCI intervention : two post stroke case series
- 2021
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Ingår i: 10th International IEEE EMBS Conference on Neural Engineering NER'21. - 9781728143378 ; , s. 817-821
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Konferensbidrag (refereegranskat)abstract
- Abstract— Clinical Electroencephalogram (EEG) Brain- Computer-Interface (BCI) rehabilitation largely depend on reliable information extraction from steadily evolving brain features. Non-stationary EEG feature behavior is considered a major challenge and a lot of effort has been devoted to developing adaptive methods to accommodate for this nonstationarity. However, learning- and plasticity-related mechanisms throughout a BCI intervention are additional sources of non-stationarity, that even though expected, we know very little about. In this work, we explore the evolution of Motor Imagery (MI) information extraction across multiple sessions, in two stroke patients, using a fixed and an adaptive Support Vector Machine (SVM) model. We show different behavior of the fixed SVM model for the two patients, indicating that for one patient, relevant MI-related EEG features shifted throughout the intervention. This observation calls for further investigations to better understand the evolution and shift of features across sessions, as well as the impact of using adaptive methods from a clinical outcome perspective.
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| 17. |
- Åstrand, Elaine
(författare)
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Mapping retinal degeneration and loss-of-function in Rd-FTL mice
- 2009
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Ingår i: Investigative Ophthalmology and Visual Science. - 0146-0404 .- 1552-5783. ; 50:12, s. 5955-5964
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Tidskriftsartikel (refereegranskat)abstract
- PURPOSE. Retinitis pigmentosa (RP) is a blinding disease caused by the degeneration of photoreceptors. To further understand the process of degeneration in RP, the authors have traced activation patterns associated with rod and cone photoreceptor degeneration in a mouse model of RP METHODS. The authors used a double-mutant mouse, Rd-FTL, which contains a natural mutation, rd1, affecting the rod photoreceptors and an axon-targeted beta-galactosidase reporter system, which is under the regulation of the promoter of the c-fos gene. These mice allowed the authors to trace degeneration-related activity that corresponded to rod and cone death RESULTS. The authors traced cell death-associated activation in both rods and cones, allowing them to accurately determine the time course of cone degeneration in these mice. In the analysis of downstream activation patterns in the inner retina, they found that amacrine and ganglion cells maintain their photopic light-related activation until at least the initiation of cone degeneration. These cell populations then show increased activity during the peak time of cone cell degeneration. The authors also examined light-regulated functional activation of a subclass of amacrine cells, the dopaminergic amacrine cells. These cells showed light-induced functional activation after rod photoreceptor death and until the period of cone photoreceptor death, suggesting that they can be regulated by cone photoreceptors alone CONCLUSIONS. These findings have helped to accurately trace the periods of photoreceptor degeneration in this model of RP and show that correct light-regulated inner retinal activation is maintained until the time of cone degeneration. (Invest Ophthalmol Vis Sci. 2009;50:5955-5964) DOI: 10.1167/iovs.09-3916
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| 18. |
- Åstrand, Elaine, et al.
(författare)
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Neuronal population correlates of target selection and distractor filtering
- 2020
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Ingår i: NeuroImage. - : Academic Press Inc.. - 1053-8119 .- 1095-9572. ; 209
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Tidskriftsartikel (refereegranskat)abstract
- Frontal Eye Field (FEF) neurons discriminate between relevant and irrelevant visual stimuli and their response magnitude predicts conscious perception. How this is reflected in the spatial representation of a visual stimulus at the neuronal population level is unknown. We recorded neuronal population activity in the FEF while monkeys were performing a forced choice cued detection task with identical target and distractor stimuli. We quantified, using machine learning techniques, estimates of target and distractor location from FEF population multiunit activities. We found that the FEF population activity provides a precise single trial estimate of reported stimuli locations. Importantly, the closer this prefrontal population single trial estimate is to the veridical stimulus location, the higher the probability that the target or the distractor is reported as perceived. We show that stimulus perception is rescued by the estimate of attention allocation specifically when the latter is close enough to the actual stimulus location, thus indicating a partial independence between attention and perception. Overall, we thus show that how and what we perceive of our environment depends on the spatial precision with which this environment is coded by prefrontal neuronal populations.
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| 19. |
- Åstrand, Elaine, et al.
(författare)
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Selective visual attention to drive cognitive brain-machine interfaces : from concepts to neurofeedback and rehabilitation applications
- 2014
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Ingår i: Frontiers in Systems Neuroscience. - Switzerland : Frontiers Media SA. - 1662-5137. ; 8:144, s. 144-160
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Tidskriftsartikel (refereegranskat)abstract
- Brain-machine interfaces (BMIs) using motor cortical activity to drive an external effector like a screen cursor or a robotic arm have seen enormous success and proven their great rehabilitation potential. An emerging parallel effort is now directed to BMIs controlled by endogenouscognitive activity, also called cognitive BMIs. While more challenging, this approach opens new dimensions to the rehabilitation of cognitivedisorders. In the present work, we focus on BMIs driven by visuospatial attention signals and we provide a critical review of these studies in the light of the accumulated knowledge about the psychophysics, anatomy, and neurophysiology of visual spatial attention. Importantly, we provide a unique comparative overview of the several studies, ranging from non-invasive to invasive human and non-human primates studies, that decodeattention-related information from ongoing neuronal activity. We discuss these studies in the light of the challenges attention-driven cognitive BMIs have to face. In a second part of the review, we discuss past and current attention-based neurofeedback studies, describing both the covert effects of neurofeedback onto neuronal activity and its overt behavioral effects. Importantly, we compare neurofeedback studies based on the amplitude of cortical activity to studies based on the enhancement of cortical information content. Last, we discuss several lines of future research and applications for attention-driven cognitive brain-computer interfaces (BCIs), including the rehabilitation of cognitive deficits, restored communication in locked in patients, and open-field applications for enhanced cognition in normal subjects. The core motivation of this work is the key idea that the improvement of current cognitive BMIs for therapeutic and open field applications needs to be grounded in a proper interdisciplinary understanding of the physiology of the cognitive function of interest, be it spatial attention, working memory or any other cognitive signal.
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