P300 event related potential (ERP) component is known to be in direct relationship to a human attention for a specific task. This research propose a novel method of utilizing a P300 amplitude for assessing the workers attention.
To access the covert cognitive processes, the authors in this work used a mobile EEG device Smarting. This way, the researchers wanted to be able to quickly setup the experiment. Also, they wanted to avoid excessive cables that can induce movement artifacts. This way, they were able to focus on their hypothesis, rather than the data cleaning algorithms.
Studying individual attention at a workplace
Did you know that humans are responsible for a stunning 80% of industrial accidents? It’s easy to think that machines are the main culprits, but in fact, most accidents happen because of human errors. While engineers worked hard to make machines safer, human factors (HF) experts are still figuring out the best way to assess workers’ cognitive abilities. The reason for this is simple, the HF experts are using observational methods. This way, they cannot reach hidden cognitive processes that are happening inside the brain.
Prof. Raja Parasaruman came up with Neuroergonomics as a tool for enriching the HF field. Neuroergonomics aims to investigate the neural processes underlying human performance. This science discipline is mainly focused on studying a human brain at work.
The main EEG feature of interest was a P300 component. To compare the P300 with the performance metrics, the authors also measured the reaction-times (RTs). Their hypothesis was that P300 is negatively correlated with the RTs. This means that better performance (shorter RT) reflects higher attention of the particpants.
The study set up was a virtual work environment where participants performed an assembly task. The biggest challenges was the precise wireless synchronization between many modalities. For this purpose, they used the Lab Streaming Layer (LSL) protocol.
Correlation between P300 and reaction times differs among individuals
P300 was negatively related to the RTs on a group average, but what happens on the individual level? The authors further investigated the correlation on individual level, as we all know that humans are diverse in nature.
The interesting finding was that not all study subjects followed the expected pattern. Some individuals with prominent P300 waves showed lower performance. It turns out that attention is a complex phenomenon, and it doesn’t adhere to a one-size-fits-all. For that reason, the authors pinpointed the importance of individual analysis in neuroergonomics.
EEG confirms the sinusoidal trend of attention fluctuations
Additionally, the researchers noticed that attention levels fluctuated over time. resembling a sine-wave-like oscillation trends in both P300 amplitude and the Reaction times. This observation gave the prospect for the future work – how about a feedback system? Such a system would help workers to stay on track and be attentive, reducing the operating errors.
Introduction of Micro-breaks improves worker’s attention
As a separate paradigm within the same setup (published in this conference paper), the scientists investigated the influence of the introduction of short and frequent micro-break periods on the worker’s attention level. They did this through the investigation of the P300 amplitude before and after the inhibitory stimulus that lasted between 3 and 5 seconds.
In this way, the inhibitory stimulus served as the microbreak period.
They found that the P300 amplitude was consistently and significantly higher on the trials that followed the microbreakperiod than prior the microbreak.
Conclusions and future work
This study explains the significance of monitoring attention in the workplace. The combination of performance metrics and P300 analysis provides insights into workers’ attention. The results highlighted the importance of studying the attention on the individual level.
This discovery opens possibilities for creating safer and more efficient work environments. The study’s findings offer a promising outlook for performance optimization using mobile EEG. Understanding the attention complexity brings us a step closer to a safe work environments.
If you are interested to see the whole study, the original work can be found on Taylor & Francis Online.