This investigation will assess the utility of a novel wearable electroencephalography (EEG) headband linked to a mobile application to monitor cognitive activity post-concussion, and alert a patient when it is time to take a mental break. Personalized cloud-based machine learning algorithms will analyze EEG data in real-time for neural features indicative of mental workload and mental fatigue, and will notify a patient when it is time to rest based on these measures. It is hypothesized that this technology may provide a much needed data-driven management tool to better inform the cognitive pacing process for both patients with concussion, as well as their clinicians.

Despite advancements in the field of concussion care, the individualized nature and nuances of concussion make it a difficult condition to manage. It has been shown that both complete rest or too much activity can prolong recovery from concussion, indicating there is an ideal zone of activity that can aid in concussion recovery. Heart-rate guided sub-symptom aerobic physical activity has been shown to speed concussion recovery and provide an objective measure for patients with concussion to inform their rehabilitative physical activities. However, no such equal exists for guiding cognitive pacing.

This study will utilize a wearable EEG headband linked to a mobile application to monitor cognitive activity post-concussion, and alert a patient when it is time to take a mental break. Personalized cloud-based machine learning algorithms will analyze EEG data in real-time for neural features of mental workload and mental fatigue, and will notify a patient when it is time to rest based on these measures. These algorithms have been developed and validated on healthy participants, and refined in concussion patients in an ongoing observational investigation yet to be published. The proposed investigation is a randomized, prospective pilot study to test the early efficacy of this technology in concussion recovery compared to standard of care alone. The results of this pilot investigation will be used to inform a future large-scale clinical trial to confirm the efficacy of this technology on concussion recovery.

Inclusion Criteria: Individuals are eligible to participate if they meet all of the following criteria:

• has been diagnosed with a concussion within previous 6 weeks;
• are between >16 and <55 years of age;
• fluent in English;
• able to attend the baseline testing session within 6 weeks post-injury;
• willing and able to comply with the requirements of the study protocol, including use of intervention;
• able to access a desktop computer or laptop with a colour monitor, reliable internet connection, pointing device,(mouse or trackpad), and modern web browser for administration of ImPACT Version 4.

Exclusion Criteria: Individuals are ineligible to participate if they meet any of the following criteria:

• do not have access to a desktop computer or laptop with the technical requirements to perform ImPACT testing at their home (as described above);

• uncontrolled epilepsy (seizure within 6 months), uncontrolled chronic recurrent migraines, or other neurological disorders that may interfere with concussion recovery and assessment;

• any signs of dementia or other pre-existing cognitive impairment that would prevent them from giving free, informed consent;

• have an implantable electrical device;

• any evidence of the following in addition to concussion diagnosis:

  • documentation that the concussion they had sustained was associated with a Glasgow Coma Scale score ≤ 12 (if available),
  • any findings of trauma on neuroimaging such as brain contusion or hematoma (if available), and
  • any evidence of skull fractures of bony injuries that would require additional rehabilitation.