Analyzing and Classifying Neural Dynamics from Intracranial Electroencephalography Signals in Brain-Computer Interface Applications
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Brain-Computer Interfaces (BCIs) that rely on motor imagery currently allow subjects to control quad-copters, robotic arms, and computer cursors. Recent advancements have been made possible because of breakthroughs in fields such as electrical engineering, computer science, and neuroscience. Currently, most real-time BCIs use hand-crafted feature extractors, feature selectors, and classification algorithms. In this work, we explore the different classification algorithms currently used in electroencephalographic (EEG) signal classification and assess their performance on intracranial EEG (iEEG) data. We first discuss the motor imagery task employed using iEEG signals and find features that clearly distinguish between different classes. Second, we compare the different state-of-the-art classifiers used in EEG BCIs in terms of their error rate, computational requirements, and feature interpret-ability. Next, we show the effectiveness of these classifiers in iEEG BCIs and last, show that our new classification algorithm that is designed to use spatial, spectral, and temporal information reaches performance comparable to other state-of-the-art classifiers while also allowing increased feature interpret-ability.
General Audience Abstract
Brain-Computer Interfaces (BCIs) as the name suggests allows individuals to interact with computers using electrical activity captured from different regions of the brain. These devices have been shown to allows subjects to control a number of devices such as quad-copters, robotic arms, and computer cursors. Applications such as these obtain electrical signals from the brain using electrodes either placed non-invasively on the scalp (also known as an electroencephalographic signal, EEG) or invasively on the surface of the brain (Electrocorticographic signal, ECoG). Before a participant can effectively communicate with the computer, the computer is calibrated to recognize different signals by collecting data from the subject and learning to distinguish them using a classification algorithm. In this work, we were interested in analyzing the effectiveness of using signals obtained from deep brain structures by using electrodes place invasively (also known as intracranial EEG, iEEG). We collected iEEG data during a two hand movement task and manually analyzed the data to find regions of the brain that are most effective in allowing us to distinguish signals during movements. We later showed that this task could be automated by using classification algorithms that are borrowed from electroencephalographic (EEG) signal experiments.
- Masters Theses