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Chronic effects of electrode implantation in the brain tissue alter the neural channel signal-to-noise ratio (SNR) over time. Variability of signal quality over time poses a difficult challenge in long-term decoding of neural signals for Brain Computer Interface (BCI). Specifically, all channels observed during a neural recording session may not be observed during the next recording session. This...
Decoding mental processes in single trials is one of the prerequisites for tailoring learning paradigms, which aim at improving performance in cognitive tasks. In this study user choices are predicted in a matrix reasoning task. By employing multivariate analysis techniques we are able to show that it is possible to decode the subjects' answers prior to their response by means of ERP-based EEG data...
An effective speech brain machine interface requires selecting the best cortical recording sites and signal features for decoding speech production, but also minimal clinical risk for the patient. Motivated by this need to reduce patient risk, the purpose of this study is to detect voice activity (speech onset and offset) automatically from spatial-spectral features of electrocorticographic signals...
In this paper, we proposed a novel supervised feature extractor named as class-augmented independent component analysis (CA-ICA) whose performance can be maintained even after the input variables are varied, only if new input variables are still linear combinations of the same independent sources as old input variables were. This property can be useful in implementing an sEMG decoder robust to the...
Brain-computer interfaces that directly decode speech could restore communication to locked-in individuals. However, decoding speech from brain signals still faces many challenges. We investigated decoding of phonemes — the smallest separable parts of speech — from ECoG signals during word production. We expanded on previous efforts to identify specific phoneme by identifying phonemes by where in...
The technology underlying brain computer interfaces has recently undergone rapid development, though a variety of issues remain that are currently preventing it from becoming a viable clinical assistive tool. Though decoding of motor output has been shown to be particularly effective when using spikes, these decoders tend to degrade with the loss of subsets of these signals. One potential solution...
We tackle the problem of classifying multichannel electrocorticogram (ECoG) related to individual finger movements for a brain machine interface (BMI). For this particular aim we applied a recently developed hierarchical spatial projection framework of neural activity for feature extraction from ECoG. The algorithm extends the binary common spatial patterns algorithm to multiclass problem by constructing...
The development of interfaces linking the human nervous system with artificial devices is an important area of research. Several groups are working on the development of devices able to restore sensory-motor function in subjects affected by neurological disorders, injuries or amputations. Neural electrodes implanted in peripheral nervous system, and in particular intrafascicular electrodes, seem to...
This study examined the feasibility of decoding semantic information from human cortical activity. Four human subjects undergoing presurgical brain mapping and seizure foci localization participated in this study. Electrocorticographic (ECoG) signals were recorded while the subjects performed simple language tasks involving semantic information processing, such as a picture naming task where subjects...
Brain-Computer Interfaces (BCI) that rely upon epidural electrocorticographic signals may become a promising tool for neurorehabilitation of patients with severe hemiparatic syndromes due to cerebrovascular, traumatic or tumor-related brain damage. Here, we show in a patient-based feasibility study that online classification of arm movement intention is possible. The intention to move or to rest can...
The objective of this work is to explore the potential use of electroencephalography (EEG) as a means for silent communication by way of decoding imagined speech from measured electrical brain waves. EEG signals were recorded at University of California, Irvine (UCI) from 7 volunteer subjects imagining two syllables, /ba/ and /ku/, without speaking or performing any overt actions. Our goal is to classify...
A Brain-Computer Interface (BCI) is a specific type of human-machine interface that enables communication between a subject/patient and a computer by direct control from decoding of brain activity. This paper deals with the P300-speller application that enables to write a text based on the oddball paradigm. To improve the ergonomics and minimize the cost of such a BCI, reducing the number of electrodes...
In this study, we attempted to identify the most influential features of input data for neural decoding across different decoders. For the example of decoders, we used support vector machine (SVM), k-nearest neighbor method (KNN) and canonical discriminate analysis (CDA) and decoded the tone-induced neural activities in a rat auditory cortex into the test tone frequencies. We proposed an algorithm...
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