What You Gain from Machine Learning (ML) and Deep Learning (DL)
We’ve entered the age of “big data”, where scientific advancement and discovery opportunities are less constrained by data storage and sharing capacities. Instead, technological and scientific innovations are more constrained by our ability to expediently and effectively use this abundantly available data. In this sense, increasingly robust and sophisticated AI modeling systems prove that even the most complex data sets can be distilled into sophisticated algorithms using real-time data processing capabilities.
These algorithms and models are proving especially useful to neuroscientists and researchers hoping to make sense of and better respond to human mental processes.
The applications are endless. The useability stretches from better marketing and user experiences through facial recognition technologies to improved efficiency for individuals in juggling their cognitive workloads.
In particular, EEG and brain research company EMOTIV has demonstrated the power of ML and DL by lowering the costs of conducting this brain research while upping efficiency in data collection and analysis. In turn, this has dramatically enhanced the utility of EEG for individuals, educational and academic communities, and enterprises exploring use cases for consumer research, among others.
Slowly but surely, AI is winding its way into applications that previous generations could not have imagined, lowering cost barriers to research and paving a faster route to the technological innovations of tomorrow.
Nowhere is that more apparent than in the realm of EEG technology. By integrating advancing ML and DL modeling, neuroscientists are unlocking vast potentials in several areas, especially brain-computer interface systems and emotional recognition.
In order to understand the current status of AI models in making sense of EEG data, a few elements must be distinguished from one another conceptually. While terms like “artificial intelligence,” “machine learning,” and “deep learning” are frequently used interchangeably, there are important nuances that distinguish them.
When creative minds first understood that machines could be taught to think like humans one day, the term, Artificial Intelligence, was born. AI encompasses several sub-fields, including machine learning and deep learning.
Machine learning is a sub-field, or branch, of AI, trained using data banks to develop complex algorithms. These algorithms can then be used to make accurate predictions about new or sample data, develop highly precise classification systems for data, and in the process, help to uncover patterns and insights that would not be practical for scientists without the use of these machines.
Deep learning takes machine learning one step further by automating more aspects of the learning and training process. Deep learning algorithms can decode unstructured data sets, such as text or images, thus requiring much less human intervention. For this reason, deep learning has been described as “scalable machine learning.”
The human brain contains roughly 100 billion neurons. Fully understanding the complex relationships among these neurons and their respective synaptic connections requires the ability to look at vast amounts of brain data holistically. For decades, the ability to isolate meta-level patterns of neural circuitry from EEG data has represented the primary rate-limiting step in the utility of EEG readings.
EEG technology itself is inexpensive. The first EEG brainwave recordings were generated in the late 1800s, and the process for collecting EEG readings is noninvasive and relatively unsophisticated.
However, the costs inherent in EEG data collection and analysis have been primarily attributed to the manual labor of manually picking out extraneous artifacts picked up by EEG, which has a low signal-to-noise ratio. EEG data is complex and carries both nonlinear and nonstationary aspects. It also has factors that vary uniquely from person to person.
Researchers were forced to pre-process vast amounts of data manually to remove the unnecessary noise and account for all the different variables. Therefore, it was impractical and infeasible for quite some time to use EEG in more sophisticated tasks like emotional recognition. Still, researchers tried.
To streamline the collection and analysis of EEG brain data and reduce the cost-benefit barrier to researchers, neuroscientists developed an EEG classification processing pipeline to break down their steps, refine respective strategies and techniques, and boost the applications of EEG.
- Data pre-processing.
- Initializing the classification procedure.
- Splitting the data set for the classifier.
- Predicting the class of new data.
- Evaluating the classification model for the test data set.
While EEG is currently still one of the most cost-effective and informative methods for capturing brain activity, the utility of EEG data continues to be limited by how reliably scientists can record brain data and efficiently process those EEG recordings.
The term “big data” refers to the increasing volumes, velocities, and varieties with which modern technology enables us to collect and process data. Big data is dramatically changing the neuroscientific landscape. Simply put, we are now, more than ever, better equipped to make use of the vast amounts of data we are collecting.
Classification tasks, especially those concerned with detecting emotional states, are increasingly handled by binary and multi-label classification processes. Supervised ML algorithms learn training data, develop models and learned parameters, and then apply them to new data in order to assign each data set its respective class labels. This process eliminates the need for humans to spend time making repetitive, time-consuming decisions.
It’s easy to hear terms like “artificial intelligence” or “machine learning” and think about futuristic worlds envisioned in pop culture artifacts like the 1984 film, The Terminator. You might assume that these technologies are too complex to be understood or valuable in the ubiquitous tasks that underly your everyday life.
AI is much less sophisticated than was originally elaborated in blockbuster hits or celebrated science fiction classics like Isaac Asimov’s 1950 novel I, Robot. Even individuals outside the study of AI can understand current AI modeling and use available models in their own research.
Real-Time ML and DL Applications in EEG Research Literature
The use of ML and DL algorithms to make sense of brain data has grown substantially over recent years, as evidenced by a systematic review published in 2021 that identified peer-reviewed research aimed at developing and refining EEG-processing algorithms. Approximately 63% of the articles covered by this review were published in the past three years, suggesting that the utilization of these models in future BCI systems and ER research can be expected to grow.
In Lukas Geimen’s published article “Machine-learning-based diagnostics of EEG pathology,” he and his team investigated ML methods and their capacity to automate clinical EEG analysis. By categorizing the automated EEG models into feature-based or end-to-end approaches, they “applied the proposed feature-based framework and deep neural networks [by using] an EEG-optimized temporal convolutional network (TCN).” They found that accuracies across both approaches were surprisingly narrow, ranging from 81% to 86%. The results show that the proposed feature-based decoding framework has similar accuracy as deep neural networks.
Yannick Roy’s et al article in the Journal of Neuroengineering discusses how he and his team reviewed 154 papers that apply DL to EEG, published between January 2010 and July 2018. These papers spanned “different application domains such as epilepsy, sleep, brain-computer interfacing, and cognitive and affective monitoring.” They found that the amount of EEG data used varied in the length of time from a few minutes to several hours. However, the number of samples seen during deep-learning model training varied from a few dozen to several million. Within all this data, they found that the deep learning approaches were more accurate than the traditional baselines across all studies that utilized these.
Visualizations and analyses indicated that both approaches used similar aspects of the data, e.g., delta and theta band power at temporal electrode locations. Yannick Roy et al argue that the accuracies of current binary EEG pathology decoders could saturate nearly 90% due to the imperfect inter-rater agreement of the clinical labels and that such decoders are already clinically useful, such as in areas where clinical EEG experts are rare. They’ve proposed that the feature-based framework is available as open source, offering a new tool for EEG machine learning research.
DL has seen an exponential increase in publications, reflecting an increasing interest in this type of processing among the scientific community.
ML and DL models are yielding groundbreaking advancements in EEG technologies. When it comes to the most competitive, new-age EEG devices on the market, no company is pushing the boundaries more than EMOTIV.
EMOTIV is a bioinformatics company and pioneer in empowering the neuroscience community through the use of EEG. EMOTIV’s innovations fall under the umbrella of BCIs, also referred to as “Mind Machine Interface,” “Direct Neural Interface,” and “Brain-Machine Interface.” These technologies have been used for over a decade to track cognitive performance, monitor emotions, and control virtual and physical objects through machine learning and trained mental commands.
EMOTIV EEG headsets include EMOTIV EPOC FLEX (32-channel EEG), EMOTIV INSIGHT 2.0 (5-channel EEG), and EPOC X (14-channel EEG). Their unique algorithms detect:
EMOTIV is advancing far more than EEG headsets. They’ve helped foster an ecosystem of tools and features that can be utilized by academics, web developers, and even curious individuals with no neuroscientific background.
With EMOTIV Cortex, researchers can develop custom applications that offer users the tools to create personalized experiences and activations using real-time brain data.
Researchers and institutions can pair their EMOTIV devices with EmotivPRO, which assists in building, publishing, acquiring, and analyzing EEG data.
EmotivPRO offers an integrated analysis of post-process data using EMOTIV’s in-house, cloud-based analyzer, eliminating the need for researchers to export their recordings.
As the processing pipeline is accomplished on EMOTIV’s cloud servers, this reduces demands on your system and allows you to conserve resources. With this AI and ML EEG technology, not only do you better conserve resources, but you benefit from complex, real-time analysis of data. Accomplish more with your studies by harnessing the utility of cloud technologies that condense days of work into a matter of minutes and complete time-intensive tasks.
With its EEG headsets and applications, EMOTIV has furthered the company’s mission by empowering individuals to unlock the inner workings of their minds and accelerate global brain research.
Research institutes are discovering EMOTIV’s low-cost, remote EEG technologies. Similarly, neuroscience researchers at companies and enterprises exploring use cases for consumer research and consumer innovation are discovering the utility of EMOTIV’s EEG headsets and applications for several business-critical applications.
Want to learn more about EMOTIV? Click here to visit the website or request a demo.