Filipe Miguel Teixeira Pereira da Silva

Advances in EEG-Based biometry

This paper is focused on proving the concept that the EEG signals collected during a perception or mental task can be used for discrimination of individuals. The viability of the EEG-based person identification was successfully tested for a data base of 13 persons. Among various classifiers tested, Support Vector Machine (SVM) with Radial Basis Function (RBF) exhibits the best performance. The problem of static classification that does not take into account the temporal nature of the EEG sequence was considered by an empirical post classifier procedure. The algorithm proposed has an effect of introducing a memory into the classifier without increasing its complexity. Control of a classified access into restricted areas security systems, health disorder identification in medicine, gaining more understanding of the cognitive human brain processes in neuroscience are some of the potential applications of EEG-based biometry.

Design and Low-Level Control of a Humanoid Robot Using a Distributed Architecture Approach

This article describes methods and strategies used to develop a humanoid robot with a distributed architecture approach where centralized and local control co-exist and concur to provide robust full monitoring and efficient control of a complex system with 22 DOF. A description of the hardware is given before introducing the architecture, since that greatly influences the methods implemented for the control systems and helps in understanding the general decisions. The platform is still undergoing improvement, but the results are very promising, mainly because many potential approaches and research issues have presented themselves and will provide opportunities to test distributed control systems with possibilities that go far beyond the classical control of robots. Some practical issues of servomotor control are also considered since that turned out to be necessary before implementing higher levels of control-these are, in turn, addressed in the last part the article, which gives an example to demonstrate the possibility of keeping a humanoid robot in an upright balanced position using only local control after reaction forces on the ground.

Position/force control of biped walking robots

The paper addresses the problem of modelling and control of a biped robot by combining Cartesian based position and force control algorithms. The complete walking cycle is divided into two phases: i) single support, in which is studied the trajectory controllability based on simple motion goals and ii) exchange of support, in which the forward leg absorbs the impact and then gradually accepts the robots weight. The contact of the foot with the constrained surface is modelled through linear spring-damper systems. The systems controllability is enhanced through the insertion of a dynamic selection matrix that modifies the actuating profile in each phase. The control algorithms are simulated and their effectiveness and robustness are discussed.

A Kinect-Based Motion Capture System for Robotic Gesture Imitation

Exploring the full potential of humanoid robots requires their ability to learn, generalize and reproduce complex tasks that will be faced in dynamic environments. In recent years, significant attention has been devoted to recovering kinematic information from the human motion using a motion capture system. This paper demonstrates and evaluates the use of a Kinect-based capture system that estimates the 3D human poses and converts them into gestures imitation in a robot. The main objectives are twofold: (1) to improve the initially estimated poses through a correction method based on constraint optimization, and (2) to present a method for computing the joint angles for the upper limbs corresponding to motion data from a human demonstrator. The feasibility of the approach is demonstrated by experimental results showing the upper-limb imitation of human actions by a robot model.

EEG Signal Processing for Brain–Computer Interfaces

This chapter is focused on recent advances in electroencephalogram (EEG) signal processing for brain computer interface (BCI) design. A general overview of BCI technologies is first presented, and then the protocol for motor imagery noninvasive BCI for mobile robot control is discussed. Our ongoing research on noninvasive BCI design based not on recorded EEG but on the brain sources that originated the EEG signal is also introduced. We propose a solution to EEG-based brain source recovering by combining two techniques, a sequential Monte Carlo method for source localization and spatial filtering by beamforming for the respective source signal estimation. The EEG inverse problem is previously studded assuming that the source localization is known. In this work for the first time the problem of inverse modeling is solved simultaneously with the problem of the respective source space localization.

Reproduction of human arm movements using Kinect-based motion capture data

Transferring skills from humans to robots is an appealing way for teaching artificial systems to perform a variety of different tasks. In this context, imitation learning appears as an important approach for teaching robots due to the generation of human-like movements and the ease of teaching new tasks. This paper addresses the use of a Kinect-based human motion capture system and the reproduction of arm movements in an upper-body humanoid robot. The objectives are threefold: (1) to deal with the lack of a kinematics model that assure coherence in the recorded 3D human poses; (2) to explore the inclusion of a shoulder complex based on a parallel mechanism; and (3) to demonstrate and evaluate how two robot models can be used to reproduce the human demonstrations. Several experimental results are included showing the upper-limb reproduction of human arm movements.

Towards a human-robot interface based on the electrical activity of the brain

Recent advances in computer hardware and signal processing assert that controlling certain functions by thoughts may represent a landmark in the way we interact with many output devices. This paper exploits the possibility of achieving a communication channel between the brain and a mobile robot through the modulation of the electroencephalogram (EEG) signal during motor imagery tasks. A major concern was directed towards designing a generalized and multi-purpose framework that supports rapid prototyping of various experimental strategies and operating modes. Preliminary results of brain-state estimation using EEG signals recorded during a self-paced left/right hand movement task are also presented. The user successfully learned to operate the system and how to better perform the motor-related tasks based on outcomes produced by its mental focus.

Towards an autonomous small-size humanoid robot: design issues and control strategies

This paper presents the design considerations of a small-size humanoid robot. The design process has revealed much about the several problems, challenges and tradeoffs imposed by biped locomotion. Among them, we here focus on the control of a single leg and its behaviour when assuming a forward motion. The controller is based on simple motion goals taking into account the reaction forces between the feet and the ground. A new method is proposed which appears to be well adapted to the class of problem considered: the use of a fractional-order controller combined with a genetic algorithm for optimal tuning of the control parameters. The control algorithm is tested through several simulations and its robustness is discussed.

A Beamformer-Particle Filter Framework for Localization of Correlated EEG Sources

Electroencephalography (EEG)-based brain computer interface (BCI) is the most studied noninvasive interface to build a direct communication pathway between the brain and an external device. However, correlated noises in EEG measurements still constitute a significant challenge. Alternatively, building BCIs based on filtered brain activity source signals instead of using their surface projections, obtained from the noisy EEG signals, is a promising and not well-explored direction. In this context, finding the locations and waveforms of inner brain sources represents a crucial task for advancing source-based noninvasive BCI technologies. In this paper, we propose a novel multicore beamformer particle filter (multicore BPF) to estimate the EEG brain source spatial locations and their corresponding waveforms. In contrast to conventional (single-core) beamforming spatial filters, the developed multicore BPF considers explicitly temporal correlation among the estimated brain sources by suppressing activation from regions with interfering coherent sources. The hybrid multicore BPF brings together the advantages of both deterministic and Bayesian inverse problem algorithms in order to improve the estimation accuracy. It solves the brain activity localization problem without prior information about approximate areas of source locations. Moreover, the multicore BPF reduces the dimensionality of the problem to half compared with the PF solution, thus alleviating the curse of dimensionality problem. The results, based on generated and real EEG data, show that the proposed framework recovers correctly the dominant sources of brain activity.

Development of a hybrid humanoid platform and incorporation of the passive actuators

This paper describes the design and development of a new hybrid humanoid platform conceived to use both active and passive actuators. Power efficiency and mechanical response capability of the robot were the main concerns driving this development. Maintaining the use of off-the-shelf RC servomotors, due to their limited cost and commercial availability, the platform was nonetheless custom-designed for lightness, mechanical stiffness and prone to vast sensorial enrichment for future advanced control. Low-cost actuators may degrade and perform poorly and erroneously in demanding conditions; therefore, one major inspiration for this work relies on the potential energy storage mechanism, using elastic elements to overcome the motors limitation, avoiding their operation near the limits, while saving energy and wearing, and also obtain faster responses of the overall platform in various motion schemes and gaits. A standard simulation environment allows the initial design and future tuning of the passive actuators for several joints in motion tasks. The early simulation results show that the elastic elements approach indeed eases the actuators tasks and is a must in the future development of the new platform now presented.