scholarly journals A Study on Decoding Models for the Reconstruction of Hand Trajectories from the Human Magnetoencephalography

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Hong Gi Yeom ◽  
Wonjun Hong ◽  
Da-Yoon Kang ◽  
Chun Kee Chung ◽  
June Sic Kim ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Neural Correlates ◽  
Machine Learning Algorithms ◽  
Linear Filter ◽  
Neural Signals ◽  
Computer Interfaces ◽  
Movement Dynamics ◽  
Point To Point ◽  
Continuous Movements ◽  
Activity Design

Decoding neural signals into control outputs has been a key to the development of brain-computer interfaces (BCIs). While many studies have identified neural correlates of kinematics or applied advanced machine learning algorithms to improve decoding performance, relatively less attention has been paid to optimal design of decoding models. For generating continuous movements from neural activity, design of decoding models should address how to incorporate movement dynamics into models and how to select a model given specific BCI objectives. Considering nonlinear and independent speed characteristics, we propose a hybrid Kalman filter to decode the hand direction and speed independently. We also investigate changes in performance of different decoding models (the linear and Kalman filters) when they predict reaching movements only or predict both reach and rest. Our offline study on human magnetoencephalography (MEG) during point-to-point arm movements shows that the performance of the linear filter or the Kalman filter is affected by including resting states for training and predicting movements. However, the hybrid Kalman filter consistently outperforms others regardless of movement states. The results demonstrate that better design of decoding models is achieved by incorporating movement dynamics into modeling or selecting a model according to decoding objectives.

scholarly journals Conceptual Priming and Familiarity: Different Expressions of Memory during Recognition Testing with Distinct Neurophysiological Correlates

2010 ◽  
Vol 22 (11) ◽  
pp. 2638-2651 ◽  
Author(s):  
Joel L. Voss ◽  
Heather D. Lucas ◽  
Ken A. Paller
Keyword(s):  
Implicit Memory ◽  
Explicit Memory ◽  
Neural Correlates ◽  
Neural Responses ◽  
Neural Signals ◽  
Conceptual Information ◽  
Conceptual Priming ◽  
Familiarity And Recollection ◽  
Recognition Testing ◽  
Different Characteristics

Familiarity and recollection are qualitatively different explicit-memory phenomena evident during recognition testing. Investigations of the neurocognitive substrates of familiarity and recollection, however, have typically disregarded implicit-memory processes likely to be engaged during recognition tests. We reasoned that differential neural responses to old and new items in a recognition test may reflect either explicit or implicit memory. Putative neural correlates of familiarity in prior experiments, for example, may actually reflect contamination by implicit memory. In two experiments, we used obscure words that subjects could not formally define to tease apart electrophysiological correlates of familiarity and one form of implicit memory, conceptual priming. In Experiment 1, conceptual priming was observed for words only if they elicited meaningful associations. In Experiment 2, two distinct neural signals were observed in conjunction with familiarity-based recognition: late posterior potentials for words that both did and did not elicit meaningful associations and FN400 potentials only for the former. Given that symbolic meaning is a prerequisite for conceptual priming, the combined results specifically link late posterior potentials and FN400 potentials with familiarity and conceptual priming, respectively. These findings contradict previous interpretations of FN400 potentials as generic signals of familiarity and show that repeated stimuli in recognition tests can engender facilitated processing of conceptual information in addition to retrieval processing that leads to the awareness of memory retrieval. The different characteristics of the electrical markers of these two types of process further underscore the biological validity of the distinction between implicit memory and explicit memory.

scholarly journals Physiological properties of brain-machine interface input signals

2017 ◽  
Vol 118 (2) ◽  
pp. 1329-1343 ◽  
Author(s):  
Marc W. Slutzky ◽  
Robert D. Flint
Keyword(s):  
High Performance ◽  
Physiological Characteristics ◽  
Neural Signals ◽  
Related Information ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Physiological Properties ◽  
Input Signals ◽  
Machine Interface ◽  
And Performance

Brain-machine interfaces (BMIs), also called brain-computer interfaces (BCIs), decode neural signals and use them to control some type of external device. Despite many experimental successes and terrific demonstrations in animals and humans, a high-performance, clinically viable device has not yet been developed for widespread usage. There are many factors that impact clinical viability and BMI performance. Arguably, the first of these is the selection of brain signals used to control BMIs. In this review, we summarize the physiological characteristics and performance—including movement-related information, longevity, and stability—of multiple types of input signals that have been used in invasive BMIs to date. These include intracortical spikes as well as field potentials obtained inside the cortex, at the surface of the cortex (electrocorticography), and at the surface of the dura mater (epidural signals). We also discuss the potential for future enhancements in input signal performance, both by improving hardware and by leveraging the knowledge of the physiological characteristics of these signals to improve decoding and stability.

scholarly journals Spatiotemporal neural correlates of brain-computer interface learning

2018 ◽  
Author(s):  
Marie-Constance Corsi ◽  
Mario Chavez ◽  
Denis Schwartz ◽  
Nathalie George ◽  
Laurent Hugueville ◽  
...  
Keyword(s):  
Skill Acquisition ◽  
Real Life ◽  
Good Control ◽  
Neural Correlates ◽  
Neural Mechanisms ◽  
Human Beings ◽  
Related Activity ◽  
Computer Interfaces ◽  
Neural Processes ◽  
Communication Control

AbstractBrain-computer interfaces have been largely developed to allow communication, control, and neurofeedback in human beings. Despite their great potential, BCIs perform inconsistently across individuals. Moreover, the neural processes activated by training that enable humans to achieve good control remain poorly understood. In this study, we show that BCI skill acquisition is paralleled by a progressive reinforcement of task-related activity and by the reduction of connectivity between regions beyond those primarily targeted during the experiments. Notably, these patterns of activity and connectivity reflect growing automaticity and predict future BCI performance. Altogether, our findings provide new insights in the neural mechanisms underlying BCI learning, which have implications for the use of this technology in a broad range of real-life applications.

scholarly journals Challenges and opportunities for the future of Brain-Computer Interface in neurorehabilitation

2021 ◽  
Author(s):  
Colin Simon ◽  
David A E Bolton ◽  
Niamh Kennedy ◽  
Surjo R. Soekadar ◽  
kathy ruddy
Keyword(s):  
Best Practices ◽  
Neural Mechanisms ◽  
Neural Signals ◽  
Generate Functional ◽  
Functional Movement ◽  
Computer Interfaces ◽  
Multi Stage ◽  
Challenges And Opportunities ◽  
Clinical Interest ◽  
Unique Potential

Brain Computer Interfaces (BCI) provide a unique technological solution to circumvent the damaged motor system. For neurorehabilitation, the BCI can be used to translate neural signals associated with movement intentions into tangible feedback for the patient, when they are unable to generate functional movement themselves. Clinical interest in BCI is growing rapidly, as it would facilitate rehabilitation to commence earlier following brain damage, and provides options for patients who are unable to partake in traditional physical therapy. However, substantial challenges with existing BCI implementations have prevented its widespread adoption. Recent advances in knowledge and technology provide opportunities to facilitate a change, provided that researchers and clinicians using BCI agree on standardisation of guidelines for protocols and shared efforts to uncover mechanisms. We propose that addressing the speed and effectiveness of learning BCI control are priorities for the field, which may be improved by multimodal or multi-stage approaches harnessing more sensitive neuroimaging technologies in the early learning stages, before transitioning to more practical, mobile implementations. Clarification of the neural mechanisms that give rise to improvement in motor function is an essential next step towards justifying clinical use of BCI. In particular, quantifying the unknown contribution of non-motor mechanisms to motor recovery calls for more stringent control conditions in experimental work.Here we provide a contemporary viewpoint on the factors impeding the scalability of BCI. Further, we provide a future outlook for optimal design of the technology to best exploit its unique potential, and best practices for research and reporting of findings.

scholarly journals Physiological Correlates of Sensory Consciousness: Evidence for a Generalized Awareness Negativity

2021 ◽  
Author(s):  
Cole Dembski ◽  
Christof Koch ◽  
Michael Pitts
Keyword(s):  
Experimental Studies ◽  
Neural Correlates ◽  
Stimulus Onset ◽  
Convincing Evidence ◽  
Future Research ◽  
Neural Signals ◽  
Tactile Stimuli ◽  
Neurological Patients ◽  
Sensory Cortices ◽  
Sensory Consciousness

We critically review the recent literature on six EEG and MEG markers of the neural correlates of consciousness (NCC) for visual, auditory and tactile stimuli in neurotypical volunteers and neurological patients. After ruling out four of these as candidate NCC, we focus on two prominent evoked signals: an early, modality-specific negativity, termed the visual or auditory awareness negativity (VAN and AAN, respectively) and a late, modality-independent positivity termed the P3b. More than twelve diverse experimental studies found that the P3b is absent despite consciously seeing, hearing, or feeling stimuli, ruling out the P3b as a true NCC. In contrast, no convincing evidence for a dissociation between the awareness negativities and consciousness has been reported thus far. Furthermore, there is evidence for an equivalent signal in the tactile domain, which we term the somatosensory awareness negativity (SAN). These three neural signals are usually maximal on the side of the scalp contralateral to the evoking stimulus, above the associated sensory cortices. We conclude that the data from these three modalities is consistent with a generalized awareness negativity (GAN) correlated with perceptual consciousness that arises 120-200 ms following stimulus onset and originates from the underlying sensory cortices. The identification of this GAN points towards new, promising avenues for future research and raises an array of concrete questions that can be empirically investigated.

scholarly journals Pathways to Consumers’ Minds: Using Machine Learning and Multiple EEG Metrics to Increase Preference Prediction Above and Beyond Traditional Measurements

2018 ◽  
Author(s):  
Adam Hakim ◽  
Shira Klorfeld ◽  
Tal Sela ◽  
Doron Friedman ◽  
Maytal Shabat-Simon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Power ◽  
Rank Order ◽  
Marketing Research ◽  
Machine Learning Algorithms ◽  
Great Promise ◽  
Neural Signals ◽  
Novel Approach ◽  
First Time ◽  
Better Than

AbstractA basic aim of marketing research is to predict consumers’ preferences and the success of marketing campaigns in the general population. However, traditional behavioral measurements have various limitations, calling for novel measurements to improve predictive power. In this study, we use neural signals measured with electroencephalography (EEG) in order to overcome these limitations. We record the EEG signals of subjects, as they watched commercials of six food products. We introduce a novel approach in which instead of using one type of EEG measure, we combine several measures, and use state-of-the-art machine learning algorithms to predict subjects’ individual future preferences over the products and the commercials’ population success, as measured by their YouTube metrics. As a benchmark, we acquired measurements of the commercials’ effectiveness using a standard questionnaire commonly used in marketing research. We reached 68.5% accuracy in predicting between the most and least preferred items and a lower than chance RMSE score for predicting the rank order preferences of all six products. We also predicted the commercials’ population success better than chance. Most importantly, we demonstrate for the first time, that for all of our predictions, the EEG measurements increased the prediction power of the questionnaires. Our analyses methods and results show great promise for utilizing EEG measures by managers, marketing practitioners, and researchers, as a valuable tool for predicting subjects’ preferences and marketing campaigns’ success.

Extended Kalman Filter in 2S1 Tracked Vehicle System with Hybrid Control

2016 ◽  
Vol 248 ◽  
pp. 69-76 ◽  
Author(s):  
Andrzej Jurkiewicz ◽  
Janusz Kowal ◽  
Kamil Zając
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Ride Comfort ◽  
Hybrid Control ◽  
Suspension System ◽  
Magnetorheological Damper ◽  
Hyperbolic Model ◽  
Linear Filter ◽  
Tracked Vehicle ◽  
Active Structure

The essence of the undertaken topic is the problem of estimation of state vector in the model of 2S1 tracked vehicle suspension system through the use of Extended Kalman Filter. The use of non-linear filter has become necessary due to the magnetorheological damper located at suspension system, which has been described by hyperbolic model. Application of the damper caused the tested suspension system has become a semi-active structure in which the hybrid control was applied. The choice of this type of control stems from the fact that in the case of tracked combat vehicles in addition to the advantageous conditions of work of vehicle crew also cornering stability and the possibility of sudden acceleration or braking is important. The hybrid control allows to determine a compromise between ride comfort and stability of 2S1 platform.

scholarly journals Imagined speech can be decoded from low- and cross-frequency intracranial EEG features

2022 ◽  
Vol 13 (1) ◽  
Author(s):  
Timothée Proix ◽  
Jaime Delgado Saa ◽  
Andy Christen ◽  
Stephanie Martin ◽  
Brian N. Pasley ◽  
...  
Keyword(s):  
Speech Production ◽  
Low Frequency ◽  
Brain Computer Interfaces ◽  
Neural Signals ◽  
Intracranial Eeg ◽  
Computer Interfaces ◽  
High Frequency Activity ◽  
Low Frequency Power ◽  
Overt Speech ◽  
Frequency Power

AbstractReconstructing intended speech from neural activity using brain-computer interfaces holds great promises for people with severe speech production deficits. While decoding overt speech has progressed, decoding imagined speech has met limited success, mainly because the associated neural signals are weak and variable compared to overt speech, hence difficult to decode by learning algorithms. We obtained three electrocorticography datasets from 13 patients, with electrodes implanted for epilepsy evaluation, who performed overt and imagined speech production tasks. Based on recent theories of speech neural processing, we extracted consistent and specific neural features usable for future brain computer interfaces, and assessed their performance to discriminate speech items in articulatory, phonetic, and vocalic representation spaces. While high-frequency activity provided the best signal for overt speech, both low- and higher-frequency power and local cross-frequency contributed to imagined speech decoding, in particular in phonetic and vocalic, i.e. perceptual, spaces. These findings show that low-frequency power and cross-frequency dynamics contain key information for imagined speech decoding.

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