Single-trial classification of feedback potentials within neurofeedback training with an EEG brain-computer interface

López‐Larraz, Eduardo; Iterate, Inaki; Escolano, Carlos; García, Isabel Escobar; Montesano, Luis; Mínguez, Javier

doi:10.1109/iembs.2011.6091138

Cited by 7 publications

(7 citation statements)

References 15 publications

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“…The evaluation of BMI generalization across sessions has been studied for classification of event related potentials [15], [16], [17]. For BMI paradigms to decode motor commands, several works have studied the reduction of calibration time in new sessions by considering information from other subjects [18] or from past sessions [14], [19].…”

Section: Introductionmentioning

confidence: 99%

Brain-machine interfaces for motor rehabilitation: Is recalibration important?

López‐Larraz

Trincado-Alonso

Montesano

2015

2015 IEEE International Conference on Rehabilitation Robotics (ICORR)

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Brain-machine interfaces (BMI) allow to decode motor commands from paralyzed patients' brains and use those commands with a rehabilitative or assistive purpose. However, brain non-stationarities can affect BMI performance over time in multi-session interventions. The amount and type of data used for calibration may play an important role on the posterior decoding performance. This paper studies six different schemes for BMI calibration, considering subjectspecific and subject-transfer scenarios. Data from a five-session rehabilitation intervention with four spinal cord injury patients is used to evaluate the decoding performance of the six proposed schemes. Our results show that recording some data at the beginning of each new session to recalibrate the BMI has a positive effect, although this effect is not achieved if we do not record enough number of trials. In addition, for subject-transfer approaches it is possible to achieve similar performances to those of subject-specific approaches for some subjects, but for others, generalization is not possible. These findings constitute a step forward towards the implantation of BMI for multiple-session rehabilitation therapies.

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Section: Introductionmentioning

confidence: 99%

Brain-machine interfaces for motor rehabilitation: Is recalibration important?

López‐Larraz

Trincado-Alonso

Montesano

2015

2015 IEEE International Conference on Rehabilitation Robotics (ICORR)

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“…The second type is constituted of four strategies to achieve an online classification more adapted to the online usage of this technique in real settings. The selected classifier was a Support Vector Machine (SVM), as it has been used previously to detect feedback potentials [7], [4]. The SVM was used with a radial basis function kernel and a bandwidth dependent on the number of features.…”

Section: B Feature Extraction and Classificationmentioning

confidence: 99%

“…Such information can provide the therapist with indirect parameters of cognitive variables, such as attention, or variables related to the engagement and adherence of a subject to the therapy process [4]. In particular, it is known that feedback stimuli elicit event-related potentials that can be measured with an electroencephalogram (EEG).…”

Section: Introductionmentioning

confidence: 99%

EEG single-trial classification of visual, auditive and vibratory feedback potentials in Brain-Computer Interfaces

López‐Larraz

Creatura

Iturrate

et al. 2011

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-Feedback stimuli are fundamental components in Brain-Computer Interfaces. It is known that the presentation of feedback stimuli elicits certain brain potentials that can be measured and classified. As stimuli can be given through different sensory modalities, it is important to understand the effects of different types of feedback on brain responses and their impact on classification. This paper presents a protocol used to obtain brain potentials elicited by visual, auditive or vibrotactile feedback stimuli. Experiments were carried out with five different subjects for each modality. Four different single-trial classification strategies were compared, according to the information used to train the classifier, achieving a classification rate of approximately 80% for each modality.

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“…Overall, the calculation led to a total of 420 (30x14) ERP related features. beta [12.5, 25] Hz, and gamma band [25,40] Hz. The median spectral power and total spectral power were also computed.…”

Section: Classification Methodsmentioning

confidence: 99%

“…Several studies have focused on developing EEG-based BCI systems to evaluate momentary attentional levels and utilized it in a closed-loop structure for attention training [24] (for a review see [1]). Among these attention training studies, some reports were on regulating the sensorimotor rhythms (SMR), i.e., theta, and alpha bands which have direct connections to memory and attention training [25]. Lim et al [26] developed a BCI system for reducing attentional impairments in children with ADHD.…”

Section: Introductionmentioning

confidence: 99%

Decoding Attentional State to Faces and Scenes Using EEG Brainwaves

et al. 2019

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Attention is the ability to facilitate processing perceptually salient information while blocking the irrelevant information to an ongoing task. For example, visual attention is a complex phenomenon of searching for a target while filtering out competing stimuli. In the present study, we developed a new Brain-Computer Interface (BCI) platform to decode brainwave patterns during sustained attention in a participant. Scalp electroencephalography (EEG) signals using a wireless headset were collected in real time during a visual attention task. In our experimental protocol, we primed participants to discriminate a sequence of composite images. Each image was a fair superimposition of a scene and a face image. The participants were asked to respond to the intended subcategory (e.g., indoor scenes) while withholding their responses for the irrelevant subcategories (e.g., outdoor scenes). We developed an individualized model using machine learning techniques to decode attentional state of the participant based on their brainwaves. Our model revealed the instantaneous attention towards face and scene categories. We conducted the experiment with six volunteer participants. The average decoding accuracy of our model was about 77%, which was comparable with a former study using functional magnetic resonance imaging (fMRI). The present work was an attempt to reveal momentary level of sustained attention using EEG signals. The platform may have potential applications in visual attention evaluation and closed-loop brainwave regulation in future.

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Single-trial classification of feedback potentials within neurofeedback training with an EEG brain-computer interface

Cited by 7 publications

References 15 publications

Brain-machine interfaces for motor rehabilitation: Is recalibration important?

Brain-machine interfaces for motor rehabilitation: Is recalibration important?

EEG single-trial classification of visual, auditive and vibratory feedback potentials in Brain-Computer Interfaces

Decoding Attentional State to Faces and Scenes Using EEG Brainwaves

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