In this study we attempt to characterize this variation, which has implications for the choice of a suitable diagnostic baseline for spatiotemporal analysis of HFO activity. However, HFO occurrence can vary widely with vigilance state. Keywords: Neural signal processing, Human performance - Sleep, Neurological disorders - EpilepsyĪbstract: Recent studies show that the rate of cortical high frequency oscillations (HFOs) differentiates epileptogenic tissue in individuals with epilepsy. These results suggest that specific brain oscillations are involved in addressing background noise during speech stimuli, which may reflect differences in cognitive load between the conditions of low and high background noise.Įffect of Vigilance Changes on the Incidence of High Frequency Oscillations in the Epileptic Brain The most discriminative features were selected in the high-beta (19–30 Hz) and gamma (30-45 Hz) bands. The selected features were then classified by a Support Vector Machine with a non-linear kernel, and the classification results were validated using a leave-one-out strategy, and presented an average classification accuracy over all 33 subjects of 83% (SD=6.4%). Features vectors were selected on an individual basis by using the statistical R2 approach. To discriminate between these two conditions, features from the 64-channel EEG recordings were extracted using the power spectrum obtained by a Fast Fourier Transform. To investigate this, we conducted a study with 33 hearing impaired individuals, whose electroencephalographic (EEG) signals were recorded while listening to sentences presented in high and low levels of background noise. Keywords: Neural signal processing, Human performance - CognitionĪbstract: To gain knowledge of listening effort in adverse situations, it is important to know how the brain processes speech with different signal-to-noise ratios (SNR).
FRANCINE PHILIPS WEBCAM TRIAL
Individual Classification of Single Trial EEG Traces to Discriminate Brain Responses to Speech with Different Signal-To-Noise Ratios These results suggest that neural populations in M1 and PMd process information related to movement differently between execution and observation. Interestingly, we found that while observed movement speed is encoded in the neural population, it only alters a small proportion of the neuron’s firing rate statistics. As a result of this difference, we then wondered if neurons during movement observation exhibited a similar characteristic to those during movement execution: changing of preferred directions as a function of movement speed. We found that a majority of neurons in the primary motor cortex (M1) and dorsal premotor cortex (PMd) have statistically different firing rate statistics between movement execution and observation. By recording from putatively the same neural population, we were able to analyze and compare single neuron statistics between movement execution and observation. Here, we performed experiments where a monkey both executes and observes a center-out-and-back task within the same experimental session. However, much work still remains to understand the similarities and differences in how these neurons compute in the motor cortex during movement execution and observation. Keywords: Neural signal processing, Motor learning, neural control, and neuromuscular systems, Brain-computer/machine interfaceĪbstract: Mirror neurons, which fire during both the execution and observation of movement, are believed to play an important role in motor processing and learning. Single Neuron Firing Rate Statistics in Motor Cortex During Execution and Observation of Movement By applying this technique, we plan to detect other types of anomalies in practical situations. We recorded the brain activity of eight participants as they listened to sentences that contained semantic anomalies and found that a combination of feature selection using linear discriminant analysis and linear kernel support vector machines achieved the highest accuracy that exceeded 60%. We expand this knowledge and detect it from a single-trial ERP using machine learning techniques. Previous studies have shown that the event-related potentials (ERP) of an electroencephalogram (EEG) are evoked in the auditory and visual modalities where a semantic anomaly occurs. As our first step, we examined the semantically anomalous feelings from EEGs when participants listened to spoken sentences. Keywords: Neural signal processing, Human performance, Human performance - CognitionĪbstract: We propose a method for the automatic detection of mismatched feelings that occur in communication.