Mufti Mahmud

Advances in Brain-Inspired Cognitive Systems

This special issue focuses on recent advancements in the field of brain-inspired cognitive systems. It comprises 18 articles which are carefully selected, significantly revised versions of papers presented at the seventh International Conference on Brain-Inspired Cognitive Systems (BICS 2015) held at Hefei, China, on December 11–13, 2015. The aim of BICS 2015 was to bring together leading scientists, engineers, and educators who use analytic, syntactic, and computational methods both to understand the prodigious processing properties of biological systems and, specifically, of the brain, and to exploit such knowledge to advance computational methods toward higher levels of cognitive competence. The conference featured plenary speeches given by renowned scholars and a range of technical sessions focusing on timely topics of interest to the scientific community. Based on the recommendation of symposium organizers and reviewers, a number of authors were invited to submit revised versions of their contributions to this special issue. All articles went through a rigorous review procedure involving at least three independent experts before being accepted for publication. Chen et al. propose an efficient algorithm for actionbased pedestrian identification using hierarchical matching pursuit and order-preserving sparse coding to identify and classify features. Gepperth et al. present a novel system for performing multi-sensor fusion with experience-based learning. The authors demonstrate that near-optimal fusion can be learned and is a resource-efficient alternative to empirical estimation of joint probability distributions and Bayesian inference. They also show that their generative learning approach outperforms Bayesian optimum as it is capable of rejecting outliers through detection of systematic changes in input statistics. Mi et al. describe a novel system for occluded face recognition. They propose a new similarity matrix called ‘averaged degree of aggregation of matched pixels’ and show that their system is very robust against occlusion and competitive in terms of recognition accuracy and computation time. Jiang et al. present a cognitively distributed simultaneous localization and tracking algorithm, with applications in Gaussian distributed wireless sensor networks, based on adaptive distributed filtering for target tracking and sensor localization. Their proposed system shows higher accuracy in parameter estimation, coupled with low computational complexity. Zhao et al. propose a new method called ‘elastic matching’ for common visual pattern discovery, through inducing sparse solutions and conducting detection more robustly. Zhang et al. describe a discriminative Lasso in which sparsity and correlation are jointly considered. The method & Amir Hussain ahu@cs.stir.ac.uk

QSpike tools: a generic framework for parallel batch preprocessing of extracellular neuronal signals recorded by substrate microelectrode arrays

Micro-Electrode Arrays (MEAs) have emerged as a mature technique to investigate brain (dys)functions in vivo and in in vitro animal models. Often referred to as “smart” Petri dishes, MEAs have demonstrated a great potential particularly for medium-throughput studies in vitro, both in academic and pharmaceutical industrial contexts. Enabling rapid comparison of ionic/pharmacological/genetic manipulations with control conditions, MEAs are employed to screen compounds by monitoring non-invasively the spontaneous and evoked neuronal electrical activity in longitudinal studies, with relatively inexpensive equipment. However, in order to acquire sufficient statistical significance, recordings last up to tens of minutes and generate large amount of raw data (e.g., 60 channels/MEA, 16 bits A/D conversion, 20 kHz sampling rate: approximately 8 GB/MEA,h uncompressed). Thus, when the experimental conditions to be tested are numerous, the availability of fast, standardized, and automated signal preprocessing becomes pivotal for any subsequent analysis and data archiving. To this aim, we developed an in-house cloud-computing system, named QSpike Tools, where CPU-intensive operations, required for preprocessing of each recorded channel (e.g., filtering, multi-unit activity detection, spike-sorting, etc.), are decomposed and batch-queued to a multi-core architecture or to a computers cluster. With the commercial availability of new and inexpensive high-density MEAs, we believe that disseminating QSpike Tools might facilitate its wide adoption and customization, and inspire the creation of community-supported cloud-computing facilities for MEAs users.

SigMate: A MATLAB-based neuronal signal processing tool

Advances in neuronal probe technology to record brain activity have posed a significant challenge in performing necessary processing and analysis of the recorded data. To be able to infer meaningful conclusions from the recorded signals through these probes, sophisticated signal processing and analysis tools are required. This paper presents a MATLAB-based novel tool, ‘SigMate’, capable of performing various processing and analysis incorporating the available standard tools and our in-house custom tools. The present features include, data display (2D and 3D), baseline correction, stimulus artifact removal, noise characterization, file operations (file splitter, file concatenator, and file column rearranger), latency estimation, determination of cortical layer activation order, spike detection, spike sorting, and are gradually growing. This tool has been tested extensively for the recordings using the standard micropipettes as well as implantable neural probes based on EOSFETs (Electrolyte-Oxide-Semiconductor Field Effect Transistors) and will be made available to the community shortly.

EEG based brain-machine interface for navigation of robotic device

The highly parallel neurophysiological recordings and the increasing number of signal processing tools open up new avenues for connecting technologies directly to neuronal processes. As the understanding of the neuronal signals is taking a better shape, lot more work to perform is coming up to properly interpret and use these signals for brain-machine interfaces. A simple brain-machine interface may be able to reestablish the broken loop of the persons with motor dysfunction. With time the brain-machine interfacing is growing more complex due to the increased availability of instruments and processes for implementation. In this work, the author proposes a brain-machine interface model through a few simple processes for automated navigation and control of robotic device using the extracted features from the EEG signals based on saccadic eye movement tasks.

A Brain-Machine Interface Based on EEG: Extracted Alpha Waves Applied to Mobile Robot

The increasing number of signal processing tools for highly parallel neurophysiological recordings opens up new avenues for connecting technologies directly to neuronal processes. As the understanding is taking a better shape, lot more work to perform is coming up. A simple brain-machine interface may be able to reestablish the broken loop of the persons with motor dysfunction. With time the brain-machine interfacing is growing more complex due to the increased availability of instruments and processes for implementation. In this work, as a proof-of-principle we established a brain-machine interface through a few simple processes to control a robotic device using the alpha wave’s event-related synchronization and event-related de-synchronization extracted from EEG.

On the Way to Large-Scale and High-Resolution Brain-Chip Interfacing

Brain-chip-interfaces (BCHIs) are hybrid entities where chips and nerve cells establish a close physical interaction allowing the transfer of information in one or both directions. Typical examples are represented by multi-site-recording chips interfaced to cultured neurons, cultured/acute brain slices, or implanted “in vivo”. This paper provides an overview on recent achievements in our laboratory in the field of BCHIs leading to enhancement of signals transmission from nerve cells to chip or from chip to nerve cells with an emphasis on in vivo interfacing, either in terms of signal-to-noise ratio or of spatiotemporal resolution. Oxide-insulated chips featuring large-scale and high-resolution arrays of stimulation and recording elements are presented as a promising technology for high spatiotemporal resolution interfacing, as recently demonstrated by recordings obtained from hippocampal slices and brain cortex in implanted animals. Finally, we report on an automated tool for processing and analysis of acquired signals by BCHIs.

A Neuro-Fuzzy Control System Based on Feature Extraction of Surface Electromyogram Signal for Solar-Powered Wheelchair

This paper presents the design and implementation of a low-cost solar-powered wheelchair for physically challenged people. The signals necessary to maneuver the wheelchair are acquired from different muscles of the hand using surface electromyography (sEMG) technique. The raw sEMG signals are collected from the upper limb muscles which are then processed, characterized, and classified to extract necessary features for the generation of control signals to be used for the automated movement of the wheelchair. An artificial neural network-based classifier is constructed to classify the patterns and features extracted from the raw sEMG signals. The classification accuracy of the extracted parameters from the sEMG signals is found to be relatively high in comparison with the existing methods. The extracted parameters used to generate control signals that are then fed into a microcomputer-based control system (MiCS). A solar-powered wheelchair prototype is developed, and the above MiCS is introduced to control its maneuver using the sEMG signals. The prototype is then thoroughly tested with sEMG signals from patients of different age groups. Also, the life cycle cost analysis of the proposed wheelchair revealed that it is financially feasible and cost-effective.

Automatic detection of layer activation order in information processing pathways of rat barrel cortex under mechanical whisker stimulation

Whisking is the natural way by which rodents explore the environment. During whisking, microcircuits in the corresponding barrel columns get activated to segregate and integrate the tactile information through the information processing pathway. The local field potentials (LFPs) recorded from the barrel columns provide important information about this pathway. Different layers of the cortex get activated during this information processing, thus having precise information about the order of layer activation is desired. This work proposes an automated, computationally efficient and easy to implement method to determine the cortical layer activation for the signals recorded from barrel cortex of anesthetized rats upon mechanical whisker stimulation.

CMOS-compatible purely capacitive interfaces for high-density in-vivo recording from neural tissue

CMOS-based neural tissue in-vivo recording chips with a purely capacitive interface are presented with 256 sites resp. 256 recording channels. A 3D post-CMOS ALD-based process allows to provide a highly efficient sensor dielectric and to realize a protective insulation layer for the non-active part of the fabricated devices. A simple interconnect-efficient sensor array topology is used. Electrical characterizations and in-vivo measurements with biological content reveal proper operation of the presented approach.

A High Resolution Bi-Directional Communication through a Brain-Chip Interface

Existing brain-machine interfacing techniques allow either high precision recordings from one or a few single neurons, or low spatial resolution recordings with a sparse sampling within the networks. Through our app-roach an efficient simultaneous bidirectional communication to the brain is realized using capacitively coupled recording and stimulation sites arranged in a large 2D multi-transistor array (MTA) with 1000 elements, integrated to a planar chip at high resolution (10μm pitch and below). The aim of the present work is to evaluate the reliability of a simple-generation silicon micro-device in recording neuronal signals from rat brain. Simultaneous recording of signals using this chip from the somatosensory cortex (S1) of living rat, are compared to standard in vivo recordings with a glass micropipette. We show that the two types of signals are identical, indicating the possibility to record signals at the same time from different sites and to perform a real-time electrical imaging of the brain cortex in vivo.