LFO-modulated HFOs can be used to identify ROIs in extratemporal lobe patients. Moreover, delta-modulated HFOs may provide more accurate localization of the EZ. These ROIs may result in better surgical outcomes when used to compliment the SOZs identified by clinicians for resection.
Abstract-This paper reported initial findings on the effects of environmental noise and auditory distractions on the performance of mental state classification based on near-infrared spectroscopy (NIRS) signals recorded from the prefrontal cortex. Characterization of the performance losses due to environmental factors could provide useful information for the future development of NIRS-based brain-computer interfaces that can be taken beyond controlled laboratory settings and into everyday environments. Experiments with a hidden Markov model-based classifier showed that while significant performance could be attained in silent conditions, only chance levels of sensitivity and specificity were obtained in noisy environments. In order to achieve robustness against environment noise, two strategies were proposed and evaluated. First, physiological responses harnessed from the autonomic nervous system were used as complementary information to NIRS signals. More specifically, four physiological signals (electrodermal activity, skin temperature, blood volume pulse, and respiration effort) were collected in synchrony with the NIRS signals as the user sat at rest and/or performed music imagery tasks. Second, an acoustic monitoring technique was proposed and used to detect startle noise events, as both the prefrontal cortex and ANS are known to involuntarily respond to auditory startle stimuli. Experiments with eight participants showed that with a startle noise compensation strategy in place, performance comparable to that observed in silent conditions could be recovered with the hybrid ANS-NIRS system. Index Terms-Ambient noise, autonomic nervous system, hidden Markov models, music imagery, near-infrared spectroscopy.
High frequency oscillations (HFOs), which collectively refer to ripples (80-200 Hz) and fast ripples (>200 Hz), have been implicated as key players in epileptogenesis. However, their presence alone is not in and of itself indicative of a pathological brain state. Rather, spatial origins as well as coexistence with other neural rhythms are essential components in defining pathological HFOs. This study investigates how the phase of the delta rhythm (0.5-4 Hz) modulates the amplitude of HFOs during a seizure episode. Seven seizures recorded from three patients presenting with intractable temporal lobe epilepsy were obtained via intracranial electroencephalography (iEEG) from a 64-electrode grid. Delta modulation of the HFO rhythms was found to emerge at seizure onset and termination regardless of the dynamics present within the seizure episode itself. Moreover, the differences between delta modulating the ripple or fast ripple may be due to the sleep stage of the patient when the seizures were being recorded. Further studies exploring how this modulation changes in space across the grid may also highlight additional properties of this phenomenon. Its temporal pattern suggests that it is a potential iEEG-based biomarker for seizure state classification.
In this work, the potential of using peripheral autonomic (PA) responses as control signals for body-machine interfaces that require no physical movement was investigated. Electrodermal activity, skin temperature, heart rate and respiration rate were collected from six participants and hidden Markov models (HMMs) were used to automatically detect when a subject was performing music imagery as opposed to being at rest. Experiments were performed under controlled silent conditions as well as in the presence of continuous and startle (e.g. door slamming) ambient noise. By developing subject-specific HMMs, music imagery was detected under silent conditions with the average sensitivity and specificity of 94.2% and 93.3%, respectively. In the presence of startle noise stimuli, the system sensitivity and specificity levels of 78.8% and 80.2% were attained, respectively. In environments corrupted by continuous ambient and startle noise, the system specificity further decreased to 75.9%. To improve the system robustness against environmental noise, a startle noise detection and compensation strategy were proposed. Once in place, performance levels were shown to be comparable to those observed in silence. The obtained results suggest that PA signals, combined with HMMs, can be useful tools for the development of body-machine interfaces that allow individuals with severe motor impairments to communicate and/or to interact with their environment.
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