BCI Award 2019 Winner: BCI-based neurofeedback training for quitting smokingBCI Award 2019 Winner: BCI-based neurofeedback training for quitting smoking Dr. Junjie Bu from University of Science & Technology of China and his team developed a closed-look neurofeedback training using a brain-computer interface to help smokers to quit. Their approach reduced cigarette craving and smoking behaviour and is a promising BCI-based tool for treating addiction. This project won the 2nd place of the BCI Award 2019. Hi Junjie, you submitted your BCI research “BCI-based neurofeedback training for quitting smoking” to the BCI Award 2019 and won 2nd place. Could you briefly describe what this project was about? Yes! In this project, we developed a novel cognition-guided neurofeedback and further tested its therapeutic efficacy on nicotine addiction by a Randomized Clinical Trial. Using this neurofeedback, smokers were trained to de-activate their EEG activity patterns related to smoking cue reactivity. We found that this neurofeedback produced short-term and long-term effects on cigarette craving and smoking behaviour. In particular, the rate of smoking amount decreased as much as 38.2% during the 4-month follow-up period after only two sessions of this neurofeedback training. What was your goal? We are applying the cognition-guided neurofeedback for treating methamphetamine addiction and alcohol addiction. As we know, they are more severe than nicotine addiction. However, there is few effective treatment for them. We hope that our neurofeedback could help the patients reduce the symptoms of addiction and change their life. At the same time, based on different kinds of addiction we will optimize our neurofeedback, eg. the cognitive task, machine learning algorithm, training sessions and so on. What technologies did you use? Our cognition-guided neurofeedback consisted of two parts. First, we trained a personalized classifier to distinguish the EEG activity patterns corresponding to smoking and neutral cue reactivity using the specific cognitive task (smoking cue reactivity task). Next, during neurofeedback training, participants were asked to repeatedly and continuously deactivate their real-time EEG activity patterns of smoking cue reactivity calculated using a previously constructed classifier. How would therapy for quitting smoking look like using your invention? Well. After two visits of the neurofeedback training, smokers showed significant decrease in cigarette craving and craving-related P300 amplitudes. The rates of cigarettes smoked per day at 1 week, 1 month and 4 months follow-up decreased 30.6%, 38.2%, and 27.4% relative to baseline. How did you have the idea to work on that? My undergraduate background is biomedical engineering. I like cognitive neuroscience and hope apply my knowledge to change something in brain science especially for human. At that time I met my PhD advisor Prof. Xiaochu Zhang. He is a cognitive psychologist. Then, we used our strengths and started the neurofeedback project. Which disciplines are involved? First, we designed a cognitive task based on cognitive psychology. Then, we recorded and analysed the EEG data based on biomedical engineering. Third, we applied the machine learning algorithm for brain pattern recognition based on computer science. Finally, we designed a Randomized Clinical Trial to test the effects based on psychiatry medicine. So it is really interdisciplinary. How was it to be under the BCI Award winners? It is a great honour to win the second place. My good friend Haohao came with me to take part in the ceremony. The ceremony was great. When I heard my name, it was really surprising. At the ceremony, I was very grateful and very thankful to g.tec, the BCI Award committee, my PhD advisor Prof. Xiaochu Zhang, my current affiliation Anhui Medical University and my family. BCI-based neurofeedback training for quitting smoking 1 Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Sciences, University of Science & Technology of China, Hefei, China.
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