Papers
1. Hammerstrom, M. (2019). Portable Electroencephalographic Measurement of Clinical Decision Making. (Honours Thesis).
2. Dames, S., Young, W., Krigolson, O., & Hammerstrom, M. (Submitted). Now more than ever, It’s Time to Invest in Nurses: A mixed methods study of a 5-week Resilience Development Program. Nurse Education.
3. Krigolson, O. E., Hammerstrom, M. R., Abimvola, W., Trska, R., Wright, B. W., Hecker, K. G., & Binsted, G. (2021). Using Muse: Rapid Mobile Assessment of Brain Performance. Frontiers in Neuroscience. https://doi.org/10.3389/fnins.2021.634147
4. Hammerstrom, M. R., Ferguson, T. D., Williams, C. C., & Krigolson, O. E. (2021). What happens when right means wrong? The impact of conflict arising from competing feedback responses. Brain Research. https://doi.org/10.1016/j.brainres.2021.147393
2. Dames, S., Young, W., Krigolson, O., & Hammerstrom, M. (Submitted). Now more than ever, It’s Time to Invest in Nurses: A mixed methods study of a 5-week Resilience Development Program. Nurse Education.
3. Krigolson, O. E., Hammerstrom, M. R., Abimvola, W., Trska, R., Wright, B. W., Hecker, K. G., & Binsted, G. (2021). Using Muse: Rapid Mobile Assessment of Brain Performance. Frontiers in Neuroscience. https://doi.org/10.3389/fnins.2021.634147
4. Hammerstrom, M. R., Ferguson, T. D., Williams, C. C., & Krigolson, O. E. (2021). What happens when right means wrong? The impact of conflict arising from competing feedback responses. Brain Research. https://doi.org/10.1016/j.brainres.2021.147393
Presentations
1. Williams, C. C., Ferguson, T. D., Hammerstrom, M., Colino, F. L., Wright, B., & Krigolson, O. E. (May, 2018). Putting the Learning Back into Neural Learning Systems. Northwest Cognition and Memory, Richmond, B.C.
Abstract: Neuroimaging research has brought to light a neural system that underlies how humans learn. Most often these studies incorporate methodology in which participants perceive nonlearnable tasks to be learnable. Here, we present a series of neuroimaging experiments with learnable tasks that demonstrate how this system changes across learning, how this persists across simulated and real-world contexts, and how quickly this occurs.
2. Hammerstrom, M., Williams, C. C., Ferguson, T. D., Colino, F., Wright, B., & Krigolson, O. E. (May, 2018). Neural Learning Signals Reflect Task Performance in a Medical Context. Northwest Cognition and Memory, Richmond, B. C.
Abstract: It is important to assess neural learning systems when humans both succeed and fail at learning. In the current study, participants learned to diagnose diseases through reinforcement learning principles and were classified as learners or non-learners depending on task completion. Results demonstrated that the learners’ accuracy improved whereas the non-learners’ accuracy did not. Correspondingly, there was a change in neural learning signals in learners but not nonlearners.
3.Hammerstrom, M., Williams, C. C., Middleton, J., & Krigolson, O. E. (May, 2019). Portable EEG Measurement of Clinical Decision making. Northwest Cognition and Memory, Victoria, B.C.
Abstract: Williams and colleagues (2019) found that intuitive decisions increased alpha and decreased theta while the opposite was true for analytical judgments. Here, we sought to determine whether these systems are employed in a medical context and whether portable EEG can be used as an assessment technique. Findings replicated those of the Williams study, indicating that these strategies can be used in clinical decision making and that they can be measured portably.
4. Hammerstrom, M., Trska, R., Henri-Barghava, A., & Krigolson, O. E. (November, 2019). Applications for a Tool for Mobile Brain Health Assessment. Biomedical Engineering and Health Technology Showcase, Victoria, B.C.
Abstract: Cost-effective means of measuring brain health in the real world is imperative. Be it in the emergency wing of a hospital or the operations of industrial machinery, assessing individual brain health can be important for both individual health and safety, as well as minimizing cost of potential errors. By utilizing a portable electroencephalography (EEG) device, we may be able to utilize human event-related potentials (ERPs) in a cost-effective and reliable manner to assess individuals’ brain health by way of the ERP components and frequency analysis.
5. Hammerstrom, M., Williams, C. C., & Krigolson, O. E. (March, 2020). Measurement of Clinical Decision Making. Jaime Cassels Undergraduate Research Award, Victoria B.C.
Abstract: Many have attempted to discover the underlying cause for misdiagnosis rates in clinicians. Here, I present a comibination of behavioural and neural data in support of the hypothesis that clinicians may rely on biases even in cases that are atypical. Reaction times, accuracy, and confidence ratings revealed that partcipants who learned to diagnose patients with liver diseases were able to form biases about these diseases. However, when tested with cases that conflicted with these values, they maintained their bias strategies which resulted in misdiagnoses. Neural data showed a lack of frontal theta activity, indicating an inability to engage cognitive control to overcome response conflict.
6. Hammerstrom, M., Carey, E., Toppings, J., & Timmins, M. (March, 2020). Technology in Rehabilitation. EPHE 447: Kinesiology Seminar and Practicum, Victoria, B.C.
Abstract: In various rehabilitation settings, new advances in technology are being made to improve the therapeutic techniques available to caregivers. As these techniques become more frequently implemented, it is important for kinesiology students to learn these methods and understand how they may be relevant to our practice. Our presentation highlights exciting new implementations of technology in physical rehabilitation, focusing on applications to central nervous system damage and peripheral limb injuries. As such, our initial objective is to define rehabilitation and explain how technology can contribute to it in a broader sense. Next, we will explore different applications of technology in peripheral limb injuries, such as robotics and walking aids. Further, we will discuss technology that aids in treatment of central nervous system damage, predominantly stroke and spinal cord injury. To conclude, we will synthesize the two disciplines using a case study to demonstrate how unique cases of extensive injury can be treated.
7. Hammerstrom, M., Trska, R., Henri-Barghava, A., & Krigolson, O. E. (March, 2020). Developing a Tool for Mobile Brain Health Assessment. Precision Health + Data Science Showcase, Victoria, B.C.
Abstract: In previous presentations we have showcased our software for conducting mobile brain health assessments. By utilizing a portable electroencephalography (EEG) device, we may utilize human event-related potentials (ERPs) in a cost-effective and reliable manner to assess individuals’ brain health by way of the ERP components and frequency analysis. Here, we disseminate recent developments in applications of our assessment tools, such as our NASA Hi-SEAS project and collaboration with UBC and VIHA to assess patients with MCIs.
8. Hammestrom, M., Williams, C. C., Ferguson, T. D., Abimbola, W., & Krigolson, O. E. (March, 2020). The Effects of Sleep on Neural Learning Signals. Cognitive Neuroscience Society Annual Meeting**, Boston, MA.
Abstract: The importance of sleep has become increasingly apparent; for example, the impact of non-REM sleep on memory consolidation. Indeed, Walker (2008) demonstrated that without adequate sleep, hippocampal function is disrupted and our ability to encode new memories is markedly decreased. But what about non-hippocampal learning systems? For instance, it has recently been posited that humans rely on a reinforcement learning system within the medial-frontal cortex for behavioural optimization. Further, there is currently a lack of research investigating sleep related effects on other learning systems such as the aforementioned one within the medial-frontal cortex. Here, we sought to address this issue. Specifically, we had participants play a simple two choice “bandit” gambling game while electroencephalographic (EEG) data was recorded after obtaining data about their previous nights sleep behaviour. Post experiment, we examined the relationship between hours slept the night before and the amplitude and latency of the reward positivity – a component of the human event-related brain potential associated with feedback evaluation. Our results demonstrate a positive relationship between hours slept the night before and reward positivity amplitude. Further, we also saw sleep related effects on the latency of the reward positivity. In other words, participants with more sleep had larger and faster EEG reinforcement learning signals. Given the increasing trend in society towards diminished sleep cycles our results speak to a growing need for better sleep hygiene.
I have also linked some of the actual posters and oral presentation materials I presented. If I have updated some results or re-interpreted findings in multiple presentations, I simply posted the most recent dissemination of that project. Any questions about my results? Feel free to contact me!
**Unfortunately, due to complications involving the outbreak of the COVID-19 virus, the live meeting of CNS 2020 was cancelled and my poster was not formally presented. However, it was presented in their online conference. Click the link for my poster pdf.
Abstract: Neuroimaging research has brought to light a neural system that underlies how humans learn. Most often these studies incorporate methodology in which participants perceive nonlearnable tasks to be learnable. Here, we present a series of neuroimaging experiments with learnable tasks that demonstrate how this system changes across learning, how this persists across simulated and real-world contexts, and how quickly this occurs.
2. Hammerstrom, M., Williams, C. C., Ferguson, T. D., Colino, F., Wright, B., & Krigolson, O. E. (May, 2018). Neural Learning Signals Reflect Task Performance in a Medical Context. Northwest Cognition and Memory, Richmond, B. C.
Abstract: It is important to assess neural learning systems when humans both succeed and fail at learning. In the current study, participants learned to diagnose diseases through reinforcement learning principles and were classified as learners or non-learners depending on task completion. Results demonstrated that the learners’ accuracy improved whereas the non-learners’ accuracy did not. Correspondingly, there was a change in neural learning signals in learners but not nonlearners.
3.Hammerstrom, M., Williams, C. C., Middleton, J., & Krigolson, O. E. (May, 2019). Portable EEG Measurement of Clinical Decision making. Northwest Cognition and Memory, Victoria, B.C.
Abstract: Williams and colleagues (2019) found that intuitive decisions increased alpha and decreased theta while the opposite was true for analytical judgments. Here, we sought to determine whether these systems are employed in a medical context and whether portable EEG can be used as an assessment technique. Findings replicated those of the Williams study, indicating that these strategies can be used in clinical decision making and that they can be measured portably.
4. Hammerstrom, M., Trska, R., Henri-Barghava, A., & Krigolson, O. E. (November, 2019). Applications for a Tool for Mobile Brain Health Assessment. Biomedical Engineering and Health Technology Showcase, Victoria, B.C.
Abstract: Cost-effective means of measuring brain health in the real world is imperative. Be it in the emergency wing of a hospital or the operations of industrial machinery, assessing individual brain health can be important for both individual health and safety, as well as minimizing cost of potential errors. By utilizing a portable electroencephalography (EEG) device, we may be able to utilize human event-related potentials (ERPs) in a cost-effective and reliable manner to assess individuals’ brain health by way of the ERP components and frequency analysis.
5. Hammerstrom, M., Williams, C. C., & Krigolson, O. E. (March, 2020). Measurement of Clinical Decision Making. Jaime Cassels Undergraduate Research Award, Victoria B.C.
Abstract: Many have attempted to discover the underlying cause for misdiagnosis rates in clinicians. Here, I present a comibination of behavioural and neural data in support of the hypothesis that clinicians may rely on biases even in cases that are atypical. Reaction times, accuracy, and confidence ratings revealed that partcipants who learned to diagnose patients with liver diseases were able to form biases about these diseases. However, when tested with cases that conflicted with these values, they maintained their bias strategies which resulted in misdiagnoses. Neural data showed a lack of frontal theta activity, indicating an inability to engage cognitive control to overcome response conflict.
6. Hammerstrom, M., Carey, E., Toppings, J., & Timmins, M. (March, 2020). Technology in Rehabilitation. EPHE 447: Kinesiology Seminar and Practicum, Victoria, B.C.
Abstract: In various rehabilitation settings, new advances in technology are being made to improve the therapeutic techniques available to caregivers. As these techniques become more frequently implemented, it is important for kinesiology students to learn these methods and understand how they may be relevant to our practice. Our presentation highlights exciting new implementations of technology in physical rehabilitation, focusing on applications to central nervous system damage and peripheral limb injuries. As such, our initial objective is to define rehabilitation and explain how technology can contribute to it in a broader sense. Next, we will explore different applications of technology in peripheral limb injuries, such as robotics and walking aids. Further, we will discuss technology that aids in treatment of central nervous system damage, predominantly stroke and spinal cord injury. To conclude, we will synthesize the two disciplines using a case study to demonstrate how unique cases of extensive injury can be treated.
7. Hammerstrom, M., Trska, R., Henri-Barghava, A., & Krigolson, O. E. (March, 2020). Developing a Tool for Mobile Brain Health Assessment. Precision Health + Data Science Showcase, Victoria, B.C.
Abstract: In previous presentations we have showcased our software for conducting mobile brain health assessments. By utilizing a portable electroencephalography (EEG) device, we may utilize human event-related potentials (ERPs) in a cost-effective and reliable manner to assess individuals’ brain health by way of the ERP components and frequency analysis. Here, we disseminate recent developments in applications of our assessment tools, such as our NASA Hi-SEAS project and collaboration with UBC and VIHA to assess patients with MCIs.
8. Hammestrom, M., Williams, C. C., Ferguson, T. D., Abimbola, W., & Krigolson, O. E. (March, 2020). The Effects of Sleep on Neural Learning Signals. Cognitive Neuroscience Society Annual Meeting**, Boston, MA.
Abstract: The importance of sleep has become increasingly apparent; for example, the impact of non-REM sleep on memory consolidation. Indeed, Walker (2008) demonstrated that without adequate sleep, hippocampal function is disrupted and our ability to encode new memories is markedly decreased. But what about non-hippocampal learning systems? For instance, it has recently been posited that humans rely on a reinforcement learning system within the medial-frontal cortex for behavioural optimization. Further, there is currently a lack of research investigating sleep related effects on other learning systems such as the aforementioned one within the medial-frontal cortex. Here, we sought to address this issue. Specifically, we had participants play a simple two choice “bandit” gambling game while electroencephalographic (EEG) data was recorded after obtaining data about their previous nights sleep behaviour. Post experiment, we examined the relationship between hours slept the night before and the amplitude and latency of the reward positivity – a component of the human event-related brain potential associated with feedback evaluation. Our results demonstrate a positive relationship between hours slept the night before and reward positivity amplitude. Further, we also saw sleep related effects on the latency of the reward positivity. In other words, participants with more sleep had larger and faster EEG reinforcement learning signals. Given the increasing trend in society towards diminished sleep cycles our results speak to a growing need for better sleep hygiene.
I have also linked some of the actual posters and oral presentation materials I presented. If I have updated some results or re-interpreted findings in multiple presentations, I simply posted the most recent dissemination of that project. Any questions about my results? Feel free to contact me!
**Unfortunately, due to complications involving the outbreak of the COVID-19 virus, the live meeting of CNS 2020 was cancelled and my poster was not formally presented. However, it was presented in their online conference. Click the link for my poster pdf.
Awards
1. Uvic Entrance Scholarship (2016) $500 CAD.
This scholarship is awarded to students applying to an undergradute degree at the University of Victoria with outstanding academic acheivement in secondary school.
2. National Science and Engineering Research Council (NSERC) Undergraduate Student Research Award (USRA; 2019) - $4500 CAD.
The NSERC USRA is awarded to undergraduate students seeking financial support to develop skills in research that complement their studies. As a part of my work for this award, I conducted data collection, analysis, and management for various neuroscience studies. These studies generally involved studying cognition and research methods.
3. University of Victoria Jaime Cassels Undergraduate Research Award (JCURA; 2020) $1500 CAD.
The goal of this award is to encourage undergraduates to pursue innovative and original research to enhance their learning while at the University of Victoria and to provide a valuable preparatory experience towards graduate studies or a research related career. It is granted to select undergraduate students completing a research component with their degree. As a part of my work for this award, I completed an Honours Thesis project studying the neural basis of clinical decision making. For a photo of me presenting my work at the conference, check out this page, or click the link to presentation 5 above for a copy of my poster!
4. NSERC USRA (2020) - $4500 CAD.
The NSERC USRA is awarded to undergraduate students seeking financial support to develop skills in research that complement their studies. As a part of my work for the award this year, I plan to undergo a detailed assessment of ocular EEG artifact removal techniques, and potentially develop a novel method if needed. By doing this, I can improve the quality of our EEG data, and solve one of the major problems that plagues our mobile EEG research.
The NSERC USRA is awarded to undergraduate students seeking financial support to develop skills in research that complement their studies. As a part of my work for the award this year, I plan to undergo a detailed assessment of ocular EEG artifact removal techniques, and potentially develop a novel method if needed. By doing this, I can improve the quality of our EEG data, and solve one of the major problems that plagues our mobile EEG research.