Brain-Computer Interface (BCI) Enabled Memory Training for Schizophrenia Advances in science supporting the growth and adaptability, or neuroplasticity, of human brain cells into late adulthood provide new promise for interventions designed to preserve and re habilitate brain function. The merging of brain science and computer technology has created a consumer market for software designed to train brain functions, such as memory and attention, following the rationale that brain circuitry can be strengthened like muscles in response to repetitive exercise. So called computer-based cognitive training software can be purchased privately at low cost, can be used on mobile devices, is designed to be enjoyable and motivating, and can be self-administered without clinical oversight. However, while accessibility and portability are advantages of computer-based interventions, there i s also an important and often overlooked shortcoming: it cannot be assumed that compromised brain areas, or normally expected approaches to performing cognitive training exercises, will be utilized during this training. Instead, compensatory mechanisms that have developed naturally around weakened or damaged brain tissue may be used preferentially. Therefore, as compensatory mechanisms are learned and reinforced during training, underutilization of the damaged tissue may lead to further weakening, rather than strengthening, of its natural function. The proposed research will attempt to address a critical limitation of current cognitive training software through the development of a brain-computer interface (BCI) enabled training program. Use of BCI technology in rehabilitation has primarily focused on spinal cord injury and motor neuron disease, where BCI enables the user to control external devices by brain activity transmitted via electroencephalogram (EEG). In a novel application of BCI, this project will examine how interactive control over training software functions could be used to monitor and reinforce targeted brain activity during training. This project will extend ongoing research on computer-based cognitive training in schizophrenia, which has produced a large pool of EEG recordings of patients and healthy community members performing a memory task. Analysis of archived data using advanced classification approaches will provide patterns of EEG activity associated with correct and failed memory trials, and differences in EEG that best distinguish patients from healthy comparison subjects. Next, a prototype training program will be built based on the same memory task but enhanced by a gaming environment and BCI control. Conventional EEG-based BCI software will be used for online signal processing, classification of EEG features, and communication with the training prototype. The memory training prototype will feature BCI control over trial start, difficulty level, and user response according to parameters for optimal brain activity identified in the archived data. Finally, usability and efficacy of the memory trainng prototype will be evaluated in a pilot stud y of schizophrenia and healthy volunteers under two training conditions: 1) using BCI parameters selected for optimal performance and, 2) using BCI parameters selected for suboptimal performance. The suboptimal condition will serve as a control for the active training condition, using the same BCI-enabled features but set to respond to EEG activity associated with failed memory trial performance. Primary outcomes will be based on comparison of EEG recordings and memory performance under the two training conditions, determining that targeted brain activity can be enlisted under BCI control, and that performance is enhanced when brain activity is in an optimal state. Usability and acceptability of the training prototype will be assessed by questionnaire.
This translational study integrates human neurophysiology, advanced computer science, and rehabilitation device technology. Products of this work will include new analytic methods for EEG feature detection based on machine learning and a novel BCI-enabled memory training software platform. A clinical feasibility study will be conducted to evaluate usability of this product and proof of principles guiding its development. Findings of this project could provide new insights into the remediation of a core cognitive deficit affecting individuals with schizophrenia and, thereby, enhance the rehabilitation potential of these patients. Importantly, if successful, the memory training prototype could be easily adapted for use with traumatic brain injured, neurologic, and other neuropsychiatric conditions.
| Johannesen, Jason K; Bi, Jinbo; Jiang, Ruhua et al. (2016) Machine learning identification of EEG features predicting working memory performance in schizophrenia and healthy adults. Neuropsychiatr Electrophysiol 2:3 |
