Neuroscience the New Science of Learning
The human brain may well be the most complex structure known to man. An often-cited quote attributed to Nobel Laureate Eric Kandel is that we have learned more about the human brain in the past 5 years than the prior 100 years (Kandel interview by Charlie Rose, 2012). One of the safest and most commonly used technologies to study the human brain is the electroencephalogram or EEG. The world of neuroscience changed dramatically with the introduction of the first human EEG recording in 1924. For the first time, human brain activity could be measured directly and in real-time. EEG is a head-mounted system collecting data passively regarding the electrophysiological activity of the brain and nearby muscle. The sensors used in EEG can range in size from a few centimeters to microelectrodes with sub-millimeter contact area. Dramatic advances in material sciences, electrical engineering and computational processing have resulted in an explosion of novel methods to collect and analyze the human EEG at greatly reduced costs.
EEG has become the most common method of conducting brain activity research and remains to this day the technology with the largest number of published scientific research studies on the brain. Starting in the 1970s, the neuroscience community began to develop consistent and reliable methods to measure key cognitive processes such as attention, meditation, emotional engagement and memory activation. These methods have gradually advanced to the point where there are now generally well-accepted tasks and methodologies to define which brainwave activities are associated with each of these cognitive processes. They are now measured precisely, just as temperature is measured with a thermometer. (See Examples of Primary Research Articles)
Our approach focuses on learning and brain-wellness. In applying neuroscience to learning models, we improve the efficiency and effectiveness of instruction through increased learner engagement. Stakeholders in learning will therefore place high value on attention data to know and understand whether and why a learner is not learning in an optimal way. Our focus on brain-wellness will involve the development of other cognitive or learning constructs associated with brainwave activity with the intended result being an end user’s improved cognitive abilities. Our data collection and recordings will record frequency bands associated with various neural cognitive systems in addition to attention (e.g., memory, emotional engagement and fluency). Future products will include multi-sensor arrays that allow for full head coverage, eye movement tracking and facial movements, all allowing for more detailed and refined measurements.
Nervanix develops and distributes software that interacts with EEG devices (currently headsets). These devices detect certain brainwave activity and measure neural cognitive systems (e.g. Attention) by applying a series of algorithms to the EEG signals captured. The devices transmit measurements to a Bluetooth enabled computing device where Nervanix software analyzes the data with respect to the stimuli occurring (e.g. Instruction), and then reports relevant feedback, enabling and informing specific adaptations aimed at improving the neural cognitive abilities.
This appendix lists a small sampling of the academic literature contributing to the development of Nervanix’s intellectual property.
- Astolfi, Laura et al. “The Track of Brain Activity during the Observation of TV Commercials with the High-Resolution EEG Technology.” Computational Intelligence and Neuroscience 2009 (2009): 1-8.
- Canolty, Ryan T., and Robert T. Knight. “The functional role of cross-frequency coupling.” Trends in Cognitive Sciences 14.11 (2010): 506-515.
- Curran, Tim et al. “Brain Potentials Reflect Behavioral Differences in True and False Recognition.” Journal of Cognitive Neuroscience 13.2 (2011): 201-216.
- Demiralp, Tamer et al. “Gamma amplitudes are coupled to theta phase in human EEG during visual perception.” International Journal of Psychophysiology: Official Journal of the International Organization of Psychophysiology 64.1 (2007): 24-30.
- Herrmann, Christoph S., and Robert T. Knight. “Mechanisms of human attention: event-related potentials and oscillations.” Neuroscience & Biobehavioral Reviews 25.6 (2001): 465-476
- Itti, Laurent, Christof Koch, and Ernst Niebur. “A Model of Saliency-based Visual Attention for Rapid Scene Analysis.” (1998): n. pag. 16 Feb. 2011.
- Klimesch, Wolfgang. “EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis.” Brain Research Reviews 29.2-3 (1999): 169-195.
- Knight, R T, and D Scabini. “Anatomic bases of event-related potentials and their relationship to novelty detection in humans.” Journal of Clinical Neurophysiology: Official Publication of the American Electroencephalographic Society 15.1 (1998): 3-13.
- Makeig, Scott et al. “Mining event-related brain dynamics.” Trends in Cognitive Sciences 8.5 (2004): 204-210.
- Moran, Rosalyn J. et al. “Peak Frequency in the Theta and Alpha Bands Correlates with Human Working Memory Capacity.” Frontiers in Human Neuroscience, 11: (2010):1-12.
- Pesonen, Mirka et al. “Brain oscillatory 1–30 Hz EEG ERD/ERS responses during the different stages of an auditory memory search task.” Neuroscience Letters 399.1-2 (2006): 45-50.
- Picton, T.W. et al. “Guidelines for using human event-related potentials to study cognition: Recording standards and publication criteria.” Psychophysiology 37.2 (2000): 127-152.
- Smith, Michael E., and Alan Gevins. “Attention and Brain Activity While Watching Television: Components of Viewer Engagement.” Media Psychology 6.3 (2004): 285.
- Vecchiato, Giovanni, Laura Astolfi, Alessandro Tabarrini, et al. “EEG Analysis of the Brain Activity during the Observation of Commercial, Political, or Public Service Announcements.” Computational Intelligence and Neuroscience 2010 (2010): 1-8.
- Vecchiato, Giovanni, Laura Astolfi, Fabrizio De Vico Fallani, et al. “Changes in Brain Activity During the Observation of TV Commercials by Using EEG, GSR and HR Measurements.” Brain Topography :10 May 2010.
- Voss, Joel L, and Ken A Paller. “An electrophysiological signature of unconscious recognition memory.” Nat Neurosci 12.3 (2009): 349-355.
- Voytek, Bradley et al. “Shifts in Gamma Phase–Amplitude Coupling Frequency from Theta to Alpha Over Posterior Cortex During Visual Tasks.” 4: 191, pages 1-8.
- Weiss, S, H M Müller, and P Rappelsberger. “Theta synchronization predicts efficient memory encoding of concrete and abstract nouns.” Neuroreport 11.11 (2000): 2357-2361.
- Weiss, Sabine, and Peter Rappelsberger. “EEG coherence within the 13–18 Hz band as a correlate of a distinct lexical organisation of concrete and abstract nouns in humans*1.” Neuroscience Letters 209.1 (1996): 17-20.
- Willems, Roel M, Robert Oostenveld, and Peter Hagoort. “Early decreases in alpha and gamma band power distinguish linguistic from visual information during spoken sentence comprehension.” Brain Research 1219 (2008): 78-90.