Decoding Finger Movements from ECoG Signals Using Switching Linear Models

One of the most interesting challenges in ECoG-based Brain-Machine Interface is movement prediction. Being able to perform such a prediction paves the way to high-degree precision command for a machine such as a robotic arm or robotic hands. As a witness of the BCI community increasing interest toward such a problem, the fourth BCI Competition provides a dataset which aim is to predict individual finger movements from ECoG signals. The difficulty of the problem relies on the fact that there is no simple relation between ECoG signals and finger movements. We propose in this paper, to estimate and decode these finger flexions using switching models controlled by an hidden state. Switching models can integrate prior knowledge about the decoding problem and helps in predicting fine and precise movements. Our model is thus based on a first block which estimates which finger is moving and another block which, knowing which finger is moving, predicts the movements of all other fingers. Numerical results that have been submitted to the Competition show that the model yields high decoding performances when the hidden state is well estimated. This approach achieved the second place in the BCI competition with a correlation measure between real and predicted movements of 0.42

FingerFlex: Inferring Finger Trajectories from ECoG signals

Motor brain-computer interface (BCI) development relies critically on neural time series decoding algorithms. Recent advances in deep learning architectures allow for automatic feature selection to approximate higher-order dependencies in data. This article presents the FingerFlex model - a convolutional encoder-decoder architecture adapted for finger movement regression on electrocorticographic (ECoG) brain data. State-of-the-art performance was achieved on a publicly available BCI competition IV dataset 4 with a correlation coefficient between true and predicted trajectories up to 0.74. The presented method provides the opportunity for developing fully-functional high-precision cortical motor brain-computer interfaces.Comment: 6 pages, 3 figures, 4 tables. Preprint. Under revie

Prior Knowledge Improves Decoding of Finger Flexion from Electrocorticographic Signals

Brain–computer interfaces (BCIs) use brain signals to convey a user’s intent. Some BCI approaches begin by decoding kinematic parameters of movements from brain signals, and then proceed to using these signals, in absence of movements, to allow a user to control an output. Recent results have shown that electrocorticographic (ECoG) recordings from the surface of the brain in humans can give information about kinematic parameters (e.g., hand velocity or finger flexion). The decoding approaches in these studies usually employed classical classification/regression algorithms that derive a linear mapping between brain signals and outputs. However, they typically only incorporate little prior information about the target movement parameter. In this paper, we incorporate prior knowledge using a Bayesian decoding method, and use it to decode finger flexion from ECoG signals. Specifically, we exploit the constraints that govern finger flexion and incorporate these constraints in the construction, structure, and the probabilistic functions of the prior model of a switched non-parametric dynamic system (SNDS). Given a measurement model resulting from a traditional linear regression method, we decoded finger flexion using posterior estimation that combined the prior and measurement models. Our results show that the application of the Bayesian decoding model, which incorporates prior knowledge, improves decoding performance compared to the application of a linear regression model, which does not incorporate prior knowledge. Thus, the results presented in this paper may ultimately lead to neurally controlled hand prostheses with full fine-grained finger articulation

Extraction of the Major Features of Brain Signals using Intelligent Networks

The brain-computer interface is considered one of the main tools for implementing and designing smart medical software. The analysis of brain signal data, called EEG, is one of the main tasks of smart medical diagnostic systems. While EEG signals have many components, one of the most important brain activities pursued is the P300 component. Detection of this component can help detect abnormalities and visualize the movement of organs of the body. In this research, a new method for processing EEG signals is proposed with the aim of detecting the P300 component. Major features were extracted from the BCI Competition IV EEG data set in a number of steps, i.e. normalization with the purpose of noise reduction using a median filter, feature extraction using a recurrent neural network, and classification using Twin Support Vector Machine. Then, a series of evaluation criteria were used to validate the proposed approach and compare it with similar methods. The results showed that the proposed approach has high accuracy

Comparison of tri-polar concentric ring electrodes to disc electrodes for decoding real and imaginary finger movements, A

2019 Spring.Includes bibliographical references.The electroencephalogram (EEG) is broadly used for diagnosis of brain diseases and research of brain activities. Although the EEG provides a good temporal resolution, it suffers from poor spatial resolution due to the blurring effects of volume conduction and signal-to-noise ratio. Many efforts have been devoted to the development of novel methods that can increase the EEG spatial resolution. The surface Laplacian, which is the second derivative of the surface potential, has been applied to EEG to improve the spatial resolution. Tri-polar concentric ring electrodes (TCREs) have been shown to estimate the surface Laplacian automatically with better spatial resolution than conventional disc electrodes. The aim of this research is to study how well the TCREs can be used to acquire EEG signals to decode real and imaginary finger movements. These EEG signals will be then translated into finger movements commands. We also compare the feasibility of discriminating finger movements from one hand using EEG recorded from TCREs and conventional disc electrodes. Furthermore, we evaluated two movement-related features, temporal EEG data and spectral features, in discriminating individual finger from one hand using non-invasive EEG. To do so, movement-related potentials (MRPs) are measured and analyzed from four TCREs and conventional disc electrodes while 13 subjects performed either motor execution or motor imagery of individual finger movements. The tri-polar-EEG (tEEG) and conventional EEG (cEEG) were recorded from electrodes placed according to the 10-20 International Electrode Positioning System over the motor cortex. Our results show that the TCREs achieved higher spatial resolution than conventional disc electrodes. Moreover, the results show that signals from TCREs generated higher decoding accuracy compared to signals from conventional disc electrodes. The average decoding accuracy of five-class classification for all subjects was of 70.04 ± 7.68% when we used temporal EEG data as feature and classified it using Artificial Neural Networks (ANNs) classifier. In addition, the results show that the TCRE EEG (tEEG) provides approximately a four times enhancement in the signal-to-noise ratio (SNR) compared to disc electrode signals. We also evaluated the interdependency level between neighboring electrodes from tri-polar, disc, and disc with Hjorth's Laplacian method in time and frequency domains by calculating the mutual information (MI) and coherence. The MRP signals recorded with the TCRE system have significantly less mutual information (MI) between electrodes than the conventional disc electrode system and disc electrodes with Hjorth's Laplacian method. Also, the results show that the mean coherence between neighboring tri-polar electrodes was found to be significantly smaller than disc electrode and disc electrode with Hjorth's method, especially at higher frequencies. This lower coherence in the high frequency band between neighboring tri polar electrodes suggests that the TCREs may record a more localized neuronal activity. The successful decoding of finger movements can provide extra degrees of freedom to drive brain computer interface (BCI) applications, especially for neurorehabilitation

Data-Driven Transducer Design and Identification for Internally-Paced Motor Brain Computer Interfaces: A Review

Brain-Computer Interfaces (BCIs) are systems that establish a direct communication pathway between the users' brain activity and external effectors. They offer the potential to improve the quality of life of motor-impaired patients. Motor BCIs aim to permit severely motor-impaired users to regain limb mobility by controlling orthoses or prostheses. In particular, motor BCI systems benefit patients if the decoded actions reflect the users' intentions with an accuracy that enables them to efficiently interact with their environment. One of the main challenges of BCI systems is to adapt the BCI's signal translation blocks to the user to reach a high decoding accuracy. This paper will review the literature of data-driven and user-specific transducer design and identification approaches and it focuses on internally-paced motor BCIs. In particular, continuous kinematic biomimetic and mental-task decoders are reviewed. Furthermore, static and dynamic decoding approaches, linear and non-linear decoding, offline and real-time identification algorithms are considered. The current progress and challenges related to the design of clinical-compatible motor BCI transducers are additionally discussed

Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation

Sensing enabled implantable devices and next-generation neurotechnology allow real-time adjustments of invasive neuromodulation. The identification of symptom and disease-specific biomarkers in invasive brain signal recordings has inspired the idea of demand dependent adaptive deep brain stimulation (aDBS). Expanding the clinical utility of aDBS with machine learning may hold the potential for the next breakthrough in the therapeutic success of clinical brain computer interfaces. To this end, sophisticated machine learning algorithms optimized for decoding of brain states from neural time-series must be developed. To support this venture, this review summarizes the current state of machine learning studies for invasive neurophysiology. After a brief introduction to the machine learning terminology, the transformation of brain recordings into meaningful features for decoding of symptoms and behavior is described. Commonly used machine learning models are explained and analyzed from the perspective of utility for aDBS. This is followed by a critical review on good practices for training and testing to ensure conceptual and practical generalizability for real-time adaptation in clinical settings. Finally, first studies combining machine learning with aDBS are highlighted. This review takes a glimpse into the promising future of intelligent adaptive DBS (iDBS) and concludes by identifying four key ingredients on the road for successful clinical adoption: i) multidisciplinary research teams, ii) publicly available datasets, iii) open-source algorithmic solutions and iv) strong world-wide research collaborations.Fil: Merk, Timon. Charité – Universitätsmedizin Berlin; AlemaniaFil: Peterson, Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina. Harvard Medical School; Estados UnidosFil: Köhler, Richard. Charité – Universitätsmedizin Berlin; AlemaniaFil: Haufe, Stefan. Charité – Universitätsmedizin Berlin; AlemaniaFil: Richardson, R. Mark. Harvard Medical School; Estados UnidosFil: Neumann, Wolf Julian. Charité – Universitätsmedizin Berlin; Alemani