Author
Andrés Úbeda
Other affiliations: Universidad Miguel Hernández de Elche
Bio: Andrés Úbeda is an academic researcher from University of Alicante. The author has contributed to research in topics: Robotic arm & Interface (computing). The author has an hindex of 16, co-authored 74 publications receiving 783 citations. Previous affiliations of Andrés Úbeda include Universidad Miguel Hernández de Elche.
Papers
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TL;DR: A non-invasive spontaneous Brain–Machine Interface has been designed to control a robot arm through the mental activity of the users through the classification of four mental tasks in order to manage the movements of the robot.
109 citations
TL;DR: A new portable and wireless interface based on electrooculography (EOG) aimed at people with severe motor disorders that allows the movement of the eyes measuring the potential between the cornea and the retina to be detected.
Abstract: This paper describes a new portable and wireless interface based on electrooculography (EOG) aimed at people with severe motor disorders This interface allows us detecting the movement of the eyes measuring the potential between the cornea and the retina The interface uses five electrodes placed around the eyes of the user in order to register this potential A processing algorithm of the EOG signals has been developed in order to detect the movement of the eyes This interface has many advantages in comparison to commercial devices It is a cheap and small sized device with USB compatibility It does not need power supply from the network as it works with batteries and USB supply Several experiments have been done to test the electronics of the interface A first set of experiments has been performed to obtain the movement of the eyes of the user processing the signals provided by the interface In addition, the interface has been used to control a real robot arm The accuracy and time taken have been measured showing that the user is capable of controlling the robot using only his/her eyes with satisfactory results
83 citations
TL;DR: From the findings, it can be concluded that low frequency bands concentrate most of the information extracted from upper limb kinematics decoding and that decoding performance of active movements is above chance levels and mainly related to the activation of cortical motor areas.
Abstract: One of the current challenges in brain-machine interfacing is to characterize and decode upper limb kinematics from brain signals, e.g. to control a prosthetic device. Recent research work states that it is possible to do so based on low frequency EEG components. However, the validity of these results is still a matter of discussion. In this paper, we assess the feasibility of decoding upper limb kinematics from EEG signals in center-out reaching tasks during passive and active movements. The decoding of arm movement was performed using a multidimensional linear regression. Passive movements were analyzed using the same methodology to study the influence of proprioceptive sensory feedback in the decoding. Finally, we evaluated the possible advantages of classifying reaching targets, instead of continuous trajectories. The results showed that arm movement decoding was significantly above chance levels. The results also indicated that EEG slow cortical potentials carry significant information to decode active center-out movements. The classification of reached targets allowed obtaining the same conclusions with a very high accuracy. Additionally, the low decoding performance obtained from passive movements suggests that discriminant modulations of low-frequency neural activity are mainly related to the execution of movement while proprioceptive feedback is not sufficient to decode upper limb kinematics. This paper contributes to the assessment of feasibility of using linear regression methods to decode upper limb kinematics from EEG signals. From our findings, it can be concluded that low frequency bands concentrate most of the information extracted from upper limb kinematics decoding and that decoding performance of active movements is above chance levels and mainly related to the activation of cortical motor areas. We also show that the classification of reached targets from decoding approaches may be a more suitable real-time methodology than a direct decoding of hand position.
64 citations
TL;DR: The neuromechanical delay is introduced as the latency between the neural drive to muscle and force during variable-force contractions and it is shown that it is broadly modulated by the central nervous system.
Abstract: The motor unit is a neuromechanical interface that converts neural signals into mechanical force with a delay determined by neural and peripheral properties. Classically, this delay has been assess...
47 citations
TL;DR: A methodology to detect the intention to make a reaching movement with the arm in healthy subjects before the movement actually starts by measuring brain activity through electroencephalographic signals that are registered by electrodes placed over the scalp is presented.
Abstract: This paper presents a methodology to detect the intention to make a reaching movement with the arm in healthy subjects before the movement actually starts. This is done by measuring brain activity through electroencephalographic (EEG) signals that are registered by electrodes placed over the scalp. The preparation and performance of an arm movement generate a phenomenon called event-related desynchronization (ERD) in the mu and beta frequency bands. A novel methodology to characterize this cognitive process based on three sums of power spectral frequencies involved in ERD is presented. The main objective of this paper is to set the benchmark for classifiers and to choose the most convenient. The best results are obtained using an SVM classifier with around 72% accuracy. This classifier will be used in further research to generate the control commands to move a robotic exoskeleton that helps people suffering from motor disabilities to perform the movement. The final aim is that this brain-controlled robotic exoskeleton improves the current rehabilitation processes of disabled people.
41 citations
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TL;DR: The current review evaluates EEG-based BCI paradigms regarding their advantages and disadvantages from a variety of perspectives, and various EEG decoding algorithms and classification methods are evaluated.
Abstract: Advances in brain science and computer technology in the past decade have led to exciting developments in brain-computer interface (BCI), thereby making BCI a top research area in applied science. The renaissance of BCI opens new methods of neurorehabilitation for physically disabled people (e.g. paralyzed patients and amputees) and patients with brain injuries (e.g. stroke patients). Recent technological advances such as wireless recording, machine learning analysis, and real-time temporal resolution have increased interest in electroencephalographic (EEG) based BCI approaches. Many BCI studies have focused on decoding EEG signals associated with whole-body kinematics/kinetics, motor imagery, and various senses. Thus, there is a need to understand the various experimental paradigms used in EEG-based BCI systems. Moreover, given that there are many available options, it is essential to choose the most appropriate BCI application to properly manipulate a neuroprosthetic or neurorehabilitation device. The current review evaluates EEG-based BCI paradigms regarding their advantages and disadvantages from a variety of perspectives. For each paradigm, various EEG decoding algorithms and classification methods are evaluated. The applications of these paradigms with targeted patients are summarized. Finally, potential problems with EEG-based BCI systems are discussed, and possible solutions are proposed.
475 citations
TL;DR: Brain-machine interfaces research has been at the forefront of many neurophysiological discoveries, including the demonstration that, through continuous use, artificial tools can be assimilated by the primate brain's body schema.
Abstract: Brain-machine interfaces (BMIs) combine methods, approaches, and concepts derived from neurophysiology, computer science, and engineering in an effort to establish real-time bidirectional links bet...
373 citations
TL;DR: This study found that a group of 13 human subjects could willingly modulate brain activity to control a robotic arm with high accuracy for performing tasks requiring multiple degrees of freedom by combination of two sequential low dimensional controls.
Abstract: Brain-computer interface (BCI) technologies aim to provide a bridge between the human brain and external devices. Prior research using non-invasive BCI to control virtual objects, such as computer cursors and virtual helicopters, and real-world objects, such as wheelchairs and quadcopters, has demonstrated the promise of BCI technologies. However, controlling a robotic arm to complete reach-and-grasp tasks efficiently using non-invasive BCI has yet to be shown. In this study, we found that a group of 13 human subjects could willingly modulate brain activity to control a robotic arm with high accuracy for performing tasks requiring multiple degrees of freedom by combination of two sequential low dimensional controls. Subjects were able to effectively control reaching of the robotic arm through modulation of their brain rhythms within the span of only a few training sessions and maintained the ability to control the robotic arm over multiple months. Our results demonstrate the viability of human operation of prosthetic limbs using non-invasive BCI technology.
327 citations
05 Feb 2006
TL;DR: In this article, Naive Bayes Classifiers are used for Naïve Bayes classifiers to train a classifier for classifier training, and the classifier is evaluated.
Abstract: 在Naive Bayes Classifiers模型中，要求父节点下的子节点（特征变量）之间相对独立，然而在现实世界中，特征与特征之间是非独立的、相关的。提出一种预处理方法，实验结果表明，该方法明显地提高了分类精度。
213 citations
TL;DR: This article provides a comprehensive review of the state-of-the-art of a complete BCI system and a considerable number of popular BCI applications are reviewed in terms of electrophysiological control signals, feature extraction, classification algorithms, and performance evaluation metrics.
Abstract: Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves. It is worth noting that the application of BCI is not limited to medical applications, and hence, the research in this field has gained due attention. Moreover, the significant number of related publications over the past two decades further indicates the consistent improvements and breakthroughs that have been made in this particular field. Nonetheless, it is also worth mentioning that with these improvements, new challenges are constantly discovered. This article provides a comprehensive review of the state-of-the-art of a complete BCI system. First, a brief overview of electroencephalogram (EEG)-based BCI systems is given. Secondly, a considerable number of popular BCI applications are reviewed in terms of electrophysiological control signals, feature extraction, classification algorithms, and performance evaluation metrics. Finally, the challenges to the recent BCI systems are discussed, and possible solutions to mitigate the issues are recommended.
207 citations