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- Creators: Barrett, The Honors College
- Creators: Helms Tillery, Stephen
- Creators: Rahman, Qasim
Computational algorithms can quickly and objectively find signal characteristics that may not be detectable with visual inspection, but many assume the data are stationary and/or linear, which biological data are not. An empirical mode decomposition (EMD) based algorithm was developed to detect potential seizures and tested on data collected from eight patients undergoing monitoring for focal resection surgery. EMD does not require linearity or stationarity and is data driven. The results suggest that a biological data driven algorithm could serve as a useful tool to objectively identify changes in cortical activity associated with seizures.
Next, the use of microECoG technology was investigated. Though both ECoG and microECoG grids are composed of electrodes resting on the surface of the cortex, changing the diameter of the electrodes creates non-trivial changes in the physics of the electrode-tissue interface that need to be accounted for. Experimenting with different recording configurations showed that proper grounding, referencing, and amplification are critical to obtain high quality neural signals from microECoG grids.
Finally, the relationship between data collected from the cortical surface with micro and macro electrodes was studied. Simultaneous recordings of the two electrode types showed differences in power spectra that suggest the inclusion of activity, possibly from deep structures, by macroelectrodes that is not accessible by microelectrodes.
The cocktail party effect describes the brain’s natural ability to attend to a specific voice or audio source in a crowded room. Researchers have recently attempted to recreate this ability in hearing aid design using brain signals from invasive electrocorticography electrodes. The present study aims to find neural signatures of auditory attention to achieve this same goal with noninvasive electroencephalographic (EEG) methods. Five human participants participated in an auditory attention task. Participants listened to a series of four syllables followed by a fifth syllable (probe syllable). Participants were instructed to indicate whether or not the probe syllable was one of the four syllables played immediately before the probe syllable. Trials of this task were separated into conditions of playing the syllables in silence (Signal) and in background noise (Signal With Noise), and both behavioral and EEG data were recorded. EEG signals were analyzed with event-related potential and time-frequency analysis methods. The behavioral data indicated that participants performed better on the task during the “Signal” condition, which aligns with the challenges demonstrated in the cocktail party effect. The EEG analysis showed that the alpha band’s (9-13 Hz) inter-trial coherence could potentially indicate characteristics of the attended speech signal. These preliminary results suggest that EEG time-frequency analysis has the potential to reveal the neural signatures of auditory attention, which may allow for the design of a noninvasive, EEG-based hearing aid.