Improvements of a Brain-Computer Interface Applied to a Robotic Wheelchair

Ferreira, André; Bastos, Teodiano; Sarcinelli-Filho, Mario; Sánchez, José Luis Martín; García, Juan Carlos García; Quintas, Manuel Mazo

doi:10.1007/978-3-642-11721-3_4

Cited by 15 publications

(4 citation statements)

References 8 publications

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“…On the other hand, there is a family of potentials generated on the subject's own will, which are called spontaneous potentials. Current research has already used these kinds of signals to control different devices, e.g., a robotic arm [ 9 , 10 ] or a wheelchair [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Classifiers to Detect Arm Movement Intention from EEG Signals

Planelles

Hortal

Costa

et al. 2014

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

Evaluating Classifiers to Detect Arm Movement Intention from EEG Signals

Planelles

Hortal

Costa

et al. 2014

Sensors

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“…The PSD of N EEG channels are represented by vector X, and the PSDs of the references signals are represented by the vector Y . These signals are sines and cosines of the fundamental frequencies and harmonics, as shown in Equation 3.…”

Section: Hybrid Power Spectral Density Analysis and Canonicalmentioning

confidence: 99%

“…It can be developed through some paradigms present in electroencephalogram (EEG) signals. For example, Event Related Desynchronization/Synchronization (ERD/ERS) [2] [3], Event Related Potentials (ERP) [4], Visual Evoked Potentials (VEP) [5] or Event Related Potential of Errors (ERPE) [6] can be used in BCI implementations.…”

Section: Introductionmentioning

confidence: 99%

Feature extraction techniques based on power spectrum for a SSVEP-BCI

Castillo

Müller

Caicedo

et al. 2014

2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE)

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This paper presents a comparison among three methods for Steady-State Visually Evoked Potentials (SSVEP) detection. These techniques are based on Power Spectral Density Analysis (PSDA) and Canonical Correlation Analysis (CCA). The first method estimates the signal-to-noise ratio of the power spectrum in each stimulus frequency using PSDA, which is called Traditional-PSDA. The second analysis estimates the relation between the difference of the stimulus frequency and its neighbor frequencies, using the power spectrum in these neighbor frequencies, and seeks the neighbor frequency which present the lowest relation value. This technique is referred to Ratio-PSDA. The third and final technique called Hybrid-PSDA-CCA. The performances of the methods were evaluated using a database of electroencephalogram (EEG) signals. The EEG signals were recorded from 19 volunteers, from which six people present disabilities. They were stimulated with visual stimuli flickering at 5.6, 6.4, 6.9 and 8.0 Hz. The system performance was evaluated considering the accuracy and the Information Transfer Rate (ITR) for each stimulus frequency. The results showed that the Hybrid-PSDA-CCA method achieved the best result with an average accuracy of 91.44%.

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“…To achieve a greater involvement of the patients, the use of a BMI can represent an important improvement. Several studies based on BMIs have demonstrated that people with disabilities are able to control properly systems such as a wheelchair [ 22 ], robots [ 23 ] or other devices such as a PC mouse [ 24 ] or a web browser [ 25 ]. The main objective in these works was to provide a new way to interact with the environment and facilitate daily life activities.…”

Section: Introductionmentioning

confidence: 99%

Using a brain-machine interface to control a hybrid upper limb exoskeleton during rehabilitation of patients with neurological conditions

Hortal

Planelles

Resquín

et al. 2015

J NeuroEngineering Rehabil

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BackgroundAs a consequence of the increase of cerebro-vascular accidents, the number of people suffering from motor disabilities is raising. Exoskeletons, Functional Electrical Stimulation (FES) devices and Brain-Machine Interfaces (BMIs) could be combined for rehabilitation purposes in order to improve therapy outcomes.MethodsIn this work, a system based on a hybrid upper limb exoskeleton is used for neurological rehabilitation. Reaching movements are supported by the passive exoskeleton ArmeoSpring and FES. The movement execution is triggered by an EEG-based BMI. The BMI uses two different methods to interact with the exoskeleton from the user’s brain activity. The first method relies on motor imagery tasks classification, whilst the second one is based on movement intention detection.ResultsThree healthy users and five patients with neurological conditions participated in the experiments to verify the usability of the system. Using the BMI based on motor imagery, healthy volunteers obtained an average accuracy of 82.9 ± 14.5 %, and patients obtained an accuracy of 65.3 ± 9.0 %, with a low False Positives rate (FP) (19.2 ± 10.4 % and 15.0 ± 8.4 %, respectively). On the other hand, by using the BMI based on detecting the arm movement intention, the average accuracy was 76.7 ± 13.2 % for healthy users and 71.6 ± 15.8 % for patients, with 28.7 ± 19.9 % and 21.2 ± 13.3 % of FP rate (healthy users and patients, respectively).ConclusionsThe accuracy of the results shows that the combined use of a hybrid upper limb exoskeleton and a BMI could be used for rehabilitation therapies. The advantage of this system is that the user is an active part of the rehabilitation procedure. The next step will be to verify what are the clinical benefits for the patients using this new rehabilitation procedure.

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Improvements of a Brain-Computer Interface Applied to a Robotic Wheelchair

Cited by 15 publications

References 8 publications

Evaluating Classifiers to Detect Arm Movement Intention from EEG Signals

Evaluating Classifiers to Detect Arm Movement Intention from EEG Signals

Feature extraction techniques based on power spectrum for a SSVEP-BCI

Using a brain-machine interface to control a hybrid upper limb exoskeleton during rehabilitation of patients with neurological conditions

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