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Neuromind is based on several technologies and techniques that are already widely used and scientifically validated: neurofeedback, immersive virtual reality, and mindfulness-based therapies. By combining these three elements for the first time via proprietary biomarkers, we offer a novel solution for precision neurocognitive modulation.
This article defines the science behind Neuromind technologies.
Neurofeedback (NF) is a specific use case of brain-computer interfaces, aimed at teaching a user to voluntarily control their own brain activity. This learning process occurs via sensory feedback (usually visual-auditory) that reflects the real-time fluctuations of the target brain activity (direct or interpreted via biomarkers). When the user’s brain activity reaches or exceeds target values, positive feedback is generated. Otherwise, the feedback can be either negative or absent.
In neurofeedback protocols, a targeted neural activity that underlies a behavior, cognitive performance, or specific pathology is measured and compared in real-time to a predefined threshold (resting or baseline value). Neural activity can be measured via systems such as EEG, fMRI, or NIRS, and feedback indicating the measured intensity of the neural activity is provided to the participant in visual or auditory form. The participant self-regulates the targeted neural substrates based on the feedback.
A wide variety of neurofeedback training protocols have been applied to optimize performance. Observed benefits include sustained attention, orientation and executive attention, memory (including spatial memory), reaction time, complex psychomotor performance, implicit procedural memory, mood, and well-being.
Neurofeedback training is the subject of research within professional populations. The links between operational skills, cognitive abilities (attention skills, working memory), individuals’ perceptions (level of fatigue and mental workload), and the rhythms of brain electrical activity make neurofeedback training a cutting-edge tool conducive to the improvement and/or maintenance of cognitive abilities under intense and prolonged workload conditions.
For example, a study demonstrated that it is possible to modify the average oscillation frequency of the Alpha range, progressively increasing it towards the upper limit of the frequency band (i.e., 12 Hz) in healthy subjects [3]. This particular neurofeedback training has shown very interesting potential for improving memory abilities (working memory) [4], [5], [6], as well as more specific skills such as precision task performance [7], which relies, among other things, on sustained attention (concentration). Other studies have used neurofeedback in flight simulation contexts, showing that this tool could improve aerobatic performance by nearly 25% on average by regulating vigilance levels in real-time [8]. Therefore, neurofeedback is a promising approach that can be used in a broader context of human performance optimization, particularly under conditions of intense and prolonged cognitive constraints [9].
It has been demonstrated that the lasting effects of neurofeedback training are not only reflected in the ability to influence one’s own brain activity but are also related to behavioral changes or improvements in mental performance [10].
Figure 1.
Mindfulness-Based Cognitive Therapy (MBCT) originated from Jon Kabat-Zinn’s stress reduction program at the University of Massachusetts Medical Center, initially developed to assist individuals with chronic pain and illness [10].
In the 10 years since the publication of the MBCT manual, research has primarily been focused on addressing MBCT’s effectiveness. Data from six randomized controlled trials (n = 593) indicate that MBCT is associated with a 44% reduction in depressive relapse risk compared with usual care for patients with three or more previous episodes, and are as efficient as continuous antidepressant treatment (for those who keep adhered) [11]. More recent studies have shown that mindfulness based psychotherapies of 8 weeks prevent depressive relapses for 2 years [12]. Taking into account the high rate of adherence compared to antidepressant maintenance treatment with their adverse effects it makes psychotherapy the preferable option.
However, the applications of mindfulness have since expanded across a variety of fields. In education, mindfulness helps improve focus and emotional regulation in students; in corporate settings, it is used to reduce stress, enhance decision-making, and boost creativity; and in sports, it fosters mental clarity and resilience, optimizing performance.
While traditional MBCT involves an 8-week course of meditation practice, there is growing evidence that even single-session mindfulness interventions can yield tangible benefits. Research has shown that brief mindfulness exercises can lead to immediate reductions in stress, improvements in mood, and enhanced cognitive function.
Nevertheless, conventional mindfulness-based interventions have their caveats too, such as the necessity of trained psychotherapists to guide it, overlap with the working hours or the absence of visual imagery or feedback to objectively estimate a patient’s mindfulness.
Here the recent technology advances, such as high-quality virtual reality and accessible easy-to-install physiological sensors come for help. The technology based implementation of mindfulness relies on the fact that mindfulness meditation is based on the 3 main components: attention control, emotion regulation and self-awareness, leading to eventual self-regulation [13]. The sustainable efficiency of mindfulness therapies implicates the mechanisms of neuroplasticity: the ability of the nervous system to durably modulate synaptic connections as a result of repetitive activation of the circuits [14], [15].
The technology then can play a crucial role in empowering mindfulness-based interventions in the following ways [16], [17]:
Last but not the least, the biofeedback provides a tool for therapy monitoring based on objective physiological data.
Virtual reality is a technology that allows the patient to be projected, via a computer system, into an immersive virtual world. The immersion of the patient in the virtual environment is accompanied by the feeling of “presence” which can be defined as the authentic feeling of existing in a world other than that where the body is physically located. This specificity, coupled with the ability to isolate the visual and auditory fields of the patient, has proven its effectiveness for the treatment of stress and anxiety [18], [21], [22].
There is growing evidence that interaction using biofeedback can have benefits in terms of motivation, user experience, engagement, and attentional focus. [26].
Although implementation of VR for mindfulness was already shown to be efficient [16], [18], [19], [27], [28] the introduction of biofeedback is a very recent trend which may substantially improve the potential of at home mindfulness based therapies [20], [27], [29], [30], [31], [32], [33], [34], [35].
Thus it would fulfill the growing need for a digital psychotherapy tool with advantages of convenience, simplicity, low cost and high acceptability;
The Neuromind project aims to develop an Affective Brain-Computer Interface (aBCI) that integrates emotion constructionist theory to implement a mindfulness-based approach into neurofeedback-driven virtual reality (VR). Brain-computer interfaces (BCIs) are typically defined as direct communication pathways between the brain and external devices, allowing brain signals to control external activities. In the context of aBCIs, these systems estimate human emotions or affect from brain signals, offering insight into the user’s emotional states.
Interest in the automatic detection of affective states has significantly grown in recent years, with research underscoring the vital role affect plays in human decision-making processes. Moreover, emotional regulation, which is closely linked to achieving mindfulness, can be enhanced through targeted neurofeedback approaches. The robust body of scientific research demonstrates the feasibility of detecting emotional signatures in EEG signals, where emotions are often modeled in terms of valence (ranging from negative to positive) and arousal (degree of intensity). This emotional mapping can be effectively captured with a modest number of EEG electrodes.
Additionally, the integration of eye-tracking technology in modern VR headsets allows for the monitoring of attentional control and arousal via pupillometry, further enhancing the precision of emotional detection in immersive environments.
These advancements offer promising applications for precision neurocognitive modulation through real-time, personalized emotional and attentional control..
Anton Filipchuk
Lead of scientific research, Healthy Mind
[3] B. Zoefel, R. J. Huster, and C. S. Herrmann, ‘Neurofeedback training of the upper alpha frequency band in EEG improves cognitive performance’, NeuroImage, vol. 54, no. 2, pp. 1427–1431, Jan. 2011, doi: 10.1016/j.neuroimage.2010.08.078. [4] M. Brickwedde, M. C. Krüger, and H. R. Dinse, ‘Somatosensory alpha oscillations gate perceptual learning efficiency’, Nat. Commun., vol. 10, no. 1, p. 263, Jan. 2019, doi: 10.1038/s41467-018-08012-0. [5] C. Escolano, M. Aguilar, and J. Minguez, ‘EEG-based upper alpha neurofeedback training improves working memory performance’, in 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA: IEEE, Aug. 2011, pp. 2327–2330. doi: 10.1109/IEMBS.2011.6090651. [6] Y. Lavy, T. Dwolatzky, Z. Kaplan, J. Guez, and D. Todder, ‘Neurofeedback Improves Memory and Peak Alpha Frequency in Individuals with Mild Cognitive Impairment’, Appl. Psychophysiol. Biofeedback, vol. 44, no. 1, pp. 41–49, Mar. 2019, doi: 10.1007/s10484-018-9418-0. [7] Y. Liu, S. C. Harihara Subramaniam, O. Sourina, E. Shah, J. Chua, and K. Ivanov, ‘NeuroFeedback Training for Enhancement of the Focused Attention Related to Athletic Performance in Elite Rifle Shooters’, in Transactions on Computational Science XXXII, vol. 10830, M. L. Gavrilova, C. J. K. Tan, and A. Sourin, Eds., in Lecture Notes in Computer Science, vol. 10830. , Berlin, Heidelberg: Springer Berlin Heidelberg, 2018, pp. 106–119. doi: 10.1007/978-3-662-56672-5_8. [8] J. Faller, J. Cummings, S. Saproo, and P. Sajda, ‘Regulation of arousal via online neurofeedback improves human performance in a demanding sensory-motor task’, Proc. Natl. Acad. Sci., vol. 116, no. 13, pp. 6482–6490, Mar. 2019, doi: 10.1073/pnas.1817207116. [9] F. Dehais, A. Lafont, R. Roy, and S. Fairclough, ‘A Neuroergonomics Approach to Mental Workload, Engagement and Human Performance’, Front. Neurosci., vol. 14, p. 268, Apr. 2020, doi: 10.3389/fnins.2020.00268. [10] A. Caria, R. Sitaram, and N. Birbaumer, ‘Real-Time fMRI: A Tool for Local Brain Regulation’, The Neuroscientist, vol. 18, no. 5, pp. 487–501, Oct. 2012, doi: 10.1177/1073858411407205.
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