Multifactor consciousness level assessment of participants with acquired brain injuries employing human–computer interfaces

• Main Authors: Andrzej Czyżewski, Adam Kurowski, Piotr Odya, Piotr Szczuko
• Format: Article
• Language: English
• Published: BMC 2020-01-01
• Series: BioMedical Engineering OnLine
• Subjects: Acquired brainstem response; Multimedia computers; Human–computer interfaces; Eye tracking; Electroencephalography; Auditory brainstem injuries
• Online Access: https://doi.org/10.1186/s12938-019-0746-y

Abstract Background A lack of communication with people suffering from acquired brain injuries may lead to drawing erroneous conclusions regarding the diagnosis or therapy of patients. Information technology and neuroscience make it possible to enhance the diagnostic and rehabilitation process of patients with traumatic brain injury or post-hypoxia. In this paper, we present a new method for evaluation possibility of communication and the assessment of such patients’ state employing future generation computers extended with advanced human–machine interfaces. Methods First, the hearing abilities of 33 participants in the state of coma were evaluated using auditory brainstem response measurements (ABR). Next, a series of interactive computer-based exercise sessions were performed with the therapist’s assistance. Participants’ actions were monitored with an eye-gaze tracking (EGT) device and with an electroencephalogram EEG monitoring headset. The data gathered were processed with the use of data clustering techniques. Results Analysis showed that the data gathered and the computer-based methods developed for their processing are suitable for evaluating the participants’ responses to stimuli. Parameters obtained from EEG signals and eye-tracker data were correlated with Glasgow Coma Scale (GCS) scores and enabled separation between GCS-related classes. The results show that in the EEG and eye-tracker signals, there are specific consciousness-related states discoverable. We observe them as outliers in diagrams on the decision space generated by the autoencoder. For this reason, the numerical variable that separates particular groups of people with the same GCS is the variance of the distance of points from the cluster center that the autoencoder generates. The higher the GCS score, the greater the variance in most cases. The results proved to be statistically significant in this context. Conclusions The results indicate that the method proposed may help to assess the consciousness state of participants in an objective manner.