Cognitive Effort Monitor (CEM)

The Cognitive effort Monitor (CEM) – a tool for improving practice efficacy 

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Cognitive or emotional barriers can hinder our ability to allocate the needed cognitive effort for effective practice – for example during rehabilitation, or education.
The cognitive effort monitor is an easy-to-use tool, which enables overcoming these barriers, which hinder practice. It is based on a headband, which incorporates three electrodes that measure the electrical activity of the brain. This signal is transmitted to a mobile device or computer, and is translated into the cognitive effort index (CEI), which measures cognitive effort. Then the monitor analyzes the dynamics of the CEI to detect cognitive or emotional barriers, which may impact the practice. The practitioner inserts a structured report of the client's performance, which is used together with the analysis of the barriers to derive recommendations for improved practice.


The Need

Practice makes perfect. However, many times mental barriers may withhold effective practice. This is true in various areas, and specifically in the rehabilitation practice and in the education practice. Cognitive or emotional barriers can hinder our ability to allocate the needed cognitive effort for effective practice. The cognitive effort monitor is an easy-to-use tool, which enables overcoming these barriers.


The Solution

Monitoring cognitive effort

The cognitive effort monitor is based on a headband, which incorporates three electrodes that measure the electrical activity of the brain. This signal is transmitted to a mobile device or computer, and is translated into the cognitive effort index (CEI) – see figure 1 for illustration. The CEI is a product of the applied neurophysiology laboratory at Rambam healthcare center, following many years of multi-disciplinary research. It is probably the most established marker of its type worldwide, and is supported by heavy research with thousands of patients from multiple clinical populations.

Cognitive Effor Monitor - Figure 1

Figure 1: the hardware components of the cognitive effort monitor
A wearable EEG headset transmits the signal to a computer or a mobile device, in which the CEI is computed
.

Identifying barriers, which may lead to an ineffective mental effort
The mental barriers, which hinder effort during practice could be divided to two types: (1) cognitive barriers, and (2) emotional barriers.
Cognitive barriers prevent the client from attending to the task at hand with effective effort. This may be because the client is tired, because the task is too difficult for the client, or because the task is boring, and potentially also too easy for the client. In all of these cases there is a cognitive barrier, and the CEI will tend to be rather consistently low, in the lower third range, which leads to automatic identification of a cognitive barrier by the monitor – see figure 2 for illustration.


Figure 2: a CEI value, which is consistently in the lower third
This indicates a cognitive barrier, with reduced effort.

Emotional barriers also hinder the client’s attention to the task at hand. This may be because the client is stressed by the task. Sometimes, the client may be generally anxious or depressed, but more often, for all of us, there are specific tasks that might be stressing, even if we do not suffer from generalized anxiety or depression. For example, a patient who recovers from a stroke, and tries to walk or talk again, might be stressed because of the difficulties encountered due to the lost abilities. It might be that the patient can actually walk or talk much better, but the walk or talk practice may be perceived as a stressing reminder of the disabilities, and this may hinder performance significantly. For another example, a student with some learning disabilities might be able to perform rather well certain exercises in mathematics. But due to stressing past exposure to learning situations, the student might suffer from a “blackout” and perform poorly.
We have learned, following extensive research that the client tends to respond in one of two ways in such conditions of emotional barriers. The client may generate a sharp drop in the CEI, indicating an avoidance response, or alternatively, a sharp rise in the CEI, indicating high attention to the stressing condition. These sharp changes in the CEI lead to automatic identification of emotional barriers as well by the monitor – see figure 3 for illustration.


Figure 3: a CEI value, with sharp changes
This indicates an emotional barrier – in this case, with an avoidance response.

Combining performance data to form feedback on how to improve practice
Our focus is upon the interaction between the possible mental barriers and performance. For example, a task might lead to a cognitive barrier, and reduced cognitive effort, because it is too difficult, or because it is too easy. But, obviously, these two situations require the introduction of different changes to the practice. For emotional barriers too, it is important to consider the client’s level of performance in order to suggest useful recommendations to further practice. Therefore, the cognitive effort monitor requests the practitioner to report performance, and combines the reported performance and the analysis of mental barriers, in order to derive recommendations for improved practice. The report of performance follows the guidelines of the goal attainment scale (GAS), which is based on pre-practice specification of the expected performance outcome for the session. For example, we might expect that by the end of the session the client could walk five meters, name seven objects, or solve correctly ten multiplication problems. Then, if the client performs at the expected level by the end of the session, we consider the session performance as moderate, if the client performs above the specified level, we consider the session performance as good, and if the client performs below the specified level, we consider the session performance as lacking. The reported performance is considered together with the analysis of mental barriers to form one of nine possible recommendations – see figure 4 for illustration.


Figure 4: a derived recommendation for further practice
The recommendation is based upon the combination of the analyzed performance level, together with barriers detection.
 

 

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The CEM License is now available for direct pruchase. To Purchase a license, fill in the form below. To Purchase a license, fill in the form below.

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References


(1) A protocol for using the monitor, demonstrated with speech rehabilitation:
Gvion, A., & Shahaf, G. (2023). Real-time monitoring of barriers to patient engagement for improved rehabilitation: a protocol and representative case reports. Disability and Rehabilitation: Assistive Technology, 18(6), 849-861.

(2) Studies of the efficacy of using mental monitoring with patients during motor rehabilitation:

- Bartur, G., Joubran, K., Peleg-Shani, S., Vatine, J. J., & Shahaf, G. (2017). An EEG tool for monitoring patient engagement during stroke rehabilitation: a feasibility study. BioMed research international, 2017.

- Bartur, G., Joubran, K., Peleg-Shani, S., Vatine, J. J., & Shahaf, G. (2020). A pilot study on the electrophysiological monitoring of patient’s engagement in post-stroke physical rehabilitation. Disability and Rehabilitation: Assistive Technology, 15(4), 471-479.

(3) Further reading of studies, which monitored multiple clinical populations:

- Shahaf, G., Yariv, S., Bloch, B., Nitzan, U., Segev, A., Reshef, A., & Bloch, Y. (2017). A pilot study of possible easy-to-use electrophysiological index for early detection of antidepressive treatment non-response. Frontiers in Psychiatry, 8, 128.

- Isserles, M., Daskalakis, Z. J., George, M. S., Blumberger, D. M., Sackeim, H. A., & Shahaf, G. (2018). Simple electroencephalographic treatment-emergent marker can predict repetitive transcranial magnetic stimulation antidepressant response—A feasibility study. The journal of ECT, 34(4), 274-282.

- Shahaf, G., Nitzan, U., Erez, G., Mendelovic, S., & Bloch, Y. (2018). Monitoring attention in ADHD with an easy-to-use electrophysiological index. Frontiers in Human Neuroscience, 12, 32.

- Shahaf, G., Kuperman, P., Bloch, Y., Yariv, S., & Granovsky, Y. (2018). Monitoring migraine cycle dynamics with an easy-to-use electrophysiological marker—A pilot study. Sensors, 18(11), 3918.

- Bart, O., & Liberman, L. (2020). Sustained Attention in Exposure to Tactile Stimuli Among Children 4 to 10 Years Old With and Without Sensory Modulation Disorders. The American Journal of Occupational Therapy, 74

(4_Supplement_1), 7411505261p1-7411505261p1.

- Gvion, A., Stark, R., Bartur, G., & Shahaf, G. (2021). Behavioural and electrophysiological evaluation of the impact of different cue types upon individuals with acquired anomia. Aphasiology, 35(12), 1519-1543.

- Karpin, H., Misha, T., Herling, N. T., Bartur, G., & Shahaf, G. (2022). Bedside patient engagement monitor for rehabilitation in disorders of consciousness–demonstrative case-reports. Disability and Rehabilitation: Assistive Technology, 17(5), 539-548.

- Avirame, K., Gshur, N., Komemi, R., & Lipskaya-Velikovsky, L. (2022). A multimodal approach for the ecological investigation of sustained attention: A pilot study. Frontiers in Human Neuroscience, 16, 971314.

- Baron Shahaf, D., Weissman, A., Priven, L., & Shahaf, G. (2022). Identifying Recall Under Sedation by a Novel EEG Based Index of Attention—A Pilot Study. Frontiers in Medicine, 9, 880384.

- Yogev-Seligmann, G., Krasovsky, T., & Kafri, M. (2022). Compensatory movement strategies differentially affect attention allocation and gait parameters in persons with Parkinson’s disease. Frontiers in human neuroscience, 16, 943047.

- Vasquez, B. P., Lloyd-Kuzik, A., Santiago, A. T., Shahaf, G., & Lass, J. W. (2023). Attentional Engagement During Mobile Application Skill Learning Among Patients With Memory Impairment: A Case Series Exploration. The American Journal of Occupational Therapy, 77(1), 7701205100.

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