Classification of Movement Intention Using Independent Components of Premovement EEG

Many previous studies on brain-machine interfaces (BMIs) have focused on electroencephalography (EEG) signals elicited during motor-command execution to generate device commands. However, exploiting pre-execution brain activity related to movement intention could improve the practical applicability of BMIs. Therefore, in this study we investigated whether EEG signals occurring before movement execution could be used to classify movement intention. Six subjects performed reaching tasks that required them to move a cursor to one of four targets distributed horizontally and vertically from the center. Using independent components of EEG acquired during a premovement phase, two-class classifications were performed for left vs. right trials and top vs. bottom trials using a support vector machine. Instructions were presented visually (test) and aurally (condition). In the test condition, accuracy for a single window was about 75%, and it increased to 85% in classification using two windows. In the control condition, accuracy for a single window was about 73%, and it increased to 80% in classification using two windows. Classification results showed that a combination of two windows from different time intervals during the premovement phase improved classification performance in the both conditions compared to a single window classification. By categorizing the independent components according to spatial pattern, we found that information depending on the modality can improve classification performance. We confirmed that EEG signals occurring during movement preparation can be used to control a BMI

Restricted Minimum Error Entropy Criterion for Robust Classification

The minimum error entropy (MEE) criterion has been verified as a powerful approach for non-Gaussian signal processing and robust machine learning. However, the implementation of MEE on robust classification is rather a vacancy in the literature. The original MEE only focuses on minimizing the Renyi's quadratic entropy of the error probability distribution function (PDF), which could cause failure in noisy classification tasks. To this end, we analyze the optimal error distribution in the presence of outliers for those classifiers with continuous errors, and introduce a simple codebook to restrict MEE so that it drives the error PDF towards the desired case. Half-quadratic based optimization and convergence analysis of the new learning criterion, called restricted MEE (RMEE), are provided. Experimental results with logistic regression and extreme learning machine are presented to verify the desirable robustness of RMEE

Partial Maximum Correntropy Regression for Robust Trajectory Decoding from Noisy Epidural Electrocorticographic Signals

The Partial Least Square Regression (PLSR) exhibits admirable competence for predicting continuous variables from inter-correlated brain recordings in the brain-computer interface. However, PLSR is in essence formulated based on the least square criterion, thus, being non-robust with respect to noises. The aim of this study is to propose a new robust implementation for PLSR. To this end, the maximum correntropy criterion (MCC) is used to propose a new robust variant of PLSR, called as Partial Maximum Correntropy Regression (PMCR). The half-quadratic optimization is utilized to calculate the robust projectors for the dimensionality reduction, and the regression coefficients are optimized by a fixed-point approach. We evaluate the proposed PMCR with a synthetic example and the public Neurotycho electrocorticography (ECoG) datasets. The extensive experimental results demonstrate that, the proposed PMCR can achieve better prediction performance than the conventional PLSR and existing variants with three different performance indicators in high-dimensional and noisy regression tasks. PMCR can suppress the performance degradation caused by the adverse noise, ameliorating the decoding robustness of the brain-computer interface

Versatile Locomotion Control of a Hexapod Robot Using a Hierarchical Network of Nonlinear Oscillator Circuits

A novel hierarchical network based on coupled nonlinear oscillators is proposed for motor pattern generation in hexapod robots. Its architecture consists of a central pattern generator (CPG), producing the global leg coordination pattern, coupled with six local pattern generators, each devoted to generating the trajectory of one leg. Every node comprises a simple nonlinear oscillator and is well-suited for implementation in a standard field-programmable analog array device. The network enables versatile locomotion control based on five high-level parameters which determine the inter-oscillator coupling pattern via simple rules. The controller was realized on dedicated hardware, deployed to control an ant-like hexapod robot, and multi-sensory telemetry was performed. As a function of a single parameter, it was able to stably reproduce the canonical gaits observed in six-legged insects, namely the wave, tetrapod, and tripod gaits. A second parameter enabled driving the robot in ant-like and cockroach-like postures. Three further parameters enabled inhibiting and resuming walking, steering, and producing uncoordinated movement. Emergent phenomena were observed in the form of a multitude of intermediate gaits and of hysteresis and metastability close to a point of gait transition. The primary contributions of this paper reside in the hierarchical controller architecture and associated approach for collapsing a large set of low-level parameters, stemming from the complex hexapod kinematics, into only five high-level parameters. Such parameters can be changed dynamically, an aspect of broad practical relevance opening new avenues for driving hexapod robots via afferent signals from other circuits representing higher brain areas, or by means of suitable brain-computer interfaces. An additional contribution is the detailed characterization via telemetry of the physical robot, involving the definition of parameters which may aid future comparison with other controllers. The present results renew interest into analog CPG architectures and reinforce the generality of the connectionist approach

Wavelet-based discrimination of isolated singularities masquerading as multifractals in detrended fluctuation analyses

The robustness of two widespread multifractal analysis methods, one based on detrended fluctuation analysis and one on wavelet leaders, is discussed in the context of time-series containing non-uniform structures with only isolated singularities. Signals generated by simulated and experimentally-realized chaos generators, together with synthetic data addressing particular aspects, are taken into consideration. The results reveal essential limitations affecting the ability of both methods to correctly infer the non-multifractal nature of signals devoid of a cascade-like hierarchy of singularities. Namely, signals harboring only isolated singularities are found to artefactually give rise to broad multifractal spectra, resembling those expected in the presence of a well-developed underlying multifractal structure. Hence, there is a real risk of incorrectly inferring multifractality due to isolated singularities. The careful consideration of local scaling properties and the distribution of Hölder exponent obtained, for example, through wavelet analysis, is indispensable for rigorously assessing the presence or absence of multifractality.Fil: Oswiecimka, Pawel. Polish Academy of Sciences; ArgentinaFil: Drozdz, Stanislaw. Cracow University of Technology. Faculty of Materials Engineering and Physics; PoloniaFil: Frasca, Mattia. University of Catania. Department of Electrical Electronic and Computer Engineering; ItaliaFil: Gebarowski, Robert. Cracow University of Technology. Faculty of Materials Engineering and Physics; PoloniaFil: Yoshimura, Natsue. Tokyo Institute of Technology. Institute of Innovative Research. FIRST; JapónFil: Zunino, Luciano José. Universidad Nacional de La Plata. Facultad de Ingeniería. Departamento de Ciencias Básicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigaciones Ópticas. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones Ópticas. Universidad Nacional de La Plata. Centro de Investigaciones Ópticas; ArgentinaFil: Minati, Ludovico. Universita degli Studi di Trento; Italia. Polish Academy of Sciences; Argentin

Drosophila PQBP1 Regulates Learning Acquisition at Projection Neurons in Aversive Olfactory Conditioning

Polyglutamine tract-binding protein-1 (PQBP1) is involved in the transcription-splicing coupling, and its mutations cause a group of human mental retardation syndromes. We generated a fly model in which the Drosophila homolog of PQBP1 (dPQBP1) is repressed by insertion of piggyBac. In classical odor conditioning, learning acquisition was significantly impaired in homozygous piggyBac-inserted flies, whereas the following memory retention was completely normal. Mushroom bodies (MBs) and antennal lobes were morphologically normal in dPQBP1-mutant flies. Projection neurons (PNs) were not reduced in number and their fiber connections were not changed, whereas gene expressions including NMDA receptor subunit 1 (NR1) were decreased in PNs. Targeted double-stranded RNA-mediated silencing of dPQBP1 in PNs, but not in MBs, similarly disrupted learning acquisition. NR1 overexpression in PNs rescued the learning disturbance of dPQBP1 mutants. HDAC (histone deacetylase) inhibitors, SAHA (suberoylanilide hydroxamic acid) and PBA (phenylbutyrate), that upregulated NR1 partially rescued the learning disturbance. Collectively, these findings identify dPQBP1 as a novel gene regulating learning acquisition at PNs

Wavelet-based discrimination of isolated singularities masquerading as multifractals in detrended fluctuation analyses

The robustness of two widespread multifractal analysis methods, one based on detrended fluctuation analysis and one on wavelet leaders, is discussed in the context of time-series containing non-uniform structures with only isolated singularities. Signals generated by simulated and experimentally-realized chaos generators, together with synthetic data addressing particular aspects, are taken into consideration. The results reveal essential limitations affecting the ability of both methods to correctly infer the non-multifractal nature of signals devoid of a cascade-like hierarchy of singularities. Namely, signals harboring only isolated singularities are found to artefactually give rise to broad multifractal spectra, resembling those expected in the presence of a well-developed underlying multifractal structure. Hence, there is a real risk of incorrectly inferring multifractality due to isolated singularities. The careful consideration of local scaling properties and the distribution of Holder exponent obtained, for example, through wavelet analysis, is indispensable for rigorously assessing the presence or absence of multifractality.Facultad de Ingenierí