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Brain.fm music  

Neuronal oscillations  

Cognitive states

Behind Brain.fm: Theory & Algorithms Compiled by Dr. Benjamin Morillon, Aix-Marseille Université

Music is a potent phenomenon, likely to impact cognition in various ways, the most known being pleasure inducement and aesthetic reward1. Why some type of music tends to induce a relaxing state while some other helps focus remains an open question. Brain.fm is a patented music software as a service technology that capitalizes on the latest in auditory neuroscience concepts coupled to empiri-cal developments to generate music from the ground up that helps trigger specific cognitive states. Theoretical background Neuronal oscillations control cognitive pro-cesses Neuronal oscillations correspond to regular varia-tions of electric activity in the brain. They regulate the communication between neurons. Since Ber-ger in 1929, who measured the first human Elec-troencephalographic pattern - an 8 to 12 Hz rhythm -, many oscillatory patterns have been ob-served2. Cognitive processes are associated with specific neuronal networks (see figure 1)3, and the multiple brain regions that compose a neuronal network communicate through neuronal oscilla-tions. Thus, neuronal oscillations correspond to the critical ‘middle ground’ linking single-cell neu-ron activity to behavior. Specific patterns of neu-ronal oscillations now start to be seen as finger-prints of cognitive processes4. Music entrains neuronal oscillations Internal neuronal oscillations can be synchronized (i.e. entrained) to external oscillators. An illustrative example of synchronization can be seen in a setup in which sever-al metronomes operating independently, once placed on a moving table, will synchronize (see www.youtube.com/watch?v=Aaxw4zbULMs). Once en-ergy can travel from one metronome to the other via the moving table, metronomes synchronize, and the minimal energy state of the system is reached. This example is a good metaphor of how music, that is, periodic and tem-porally structured sounds, can synchronize and entrain neuronal oscillations via traveling waves (first acoustic and then neuro-electric)5 (see Figure 2). Music can control cognitive states   Figure 2. Simplified illustration of synchroni-

zation of external and cognitive oscillations.

Figure 1. Human-brain networks. A. Default mode network. B. Somatomotor network. C. Visual network. D. Language network. E. Dorsal attention network. F. Ventral attention network. G. Frontoparietal control network3.

Table 1. Brain.fm music influences cognitive states by en-training neuronal oscillations.

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Figure 3. Amplitude-modulated music segment: (Top) 1-second music segment waveform. (Bottom) Waveform of same segment after inserting amplitude modula-tions.

Focus, relax, sleep… Each and every cognitive state, underpinned by a specific pattern of neuronal oscillations, can thus theoretically be influenced by a specific musical pattern. Regarding attention, Jones and colleagues have developed a theoretical framework, the dynamic attending theory6, stipu-lating that attention is not evenly distributed over time, but rather fluctuates (or oscillates) in time via cycles. Importantly, attentional fluctuations would be driven by the synchronization between internal (neuronal) oscillators and (external) periodicities in the environment (as music). This hypothesis, that the periodic music structure entrains the attentional oscillatory cycles (see Figure 2), has recently re-ceived ample empirical support7,8. Brain.fm algorithms overview Brain.fm uses algorithmically generated music to modulate cognitive states. Here are the key algo-rithmic parameters and corresponding acoustic specificities uniquely present in Brain.fm music.

Table 2. Frequency range of amplitude modulations used in Brain.fm to induce each cognitive state.

Consistency of the rhythmic and frequency profiles through the musical piece: Brain.fm music operates at 120bpm (i.e. 2 Hz). As described in the dynamic attend-ing theory6, keeping a regular rhythm is key to synchro-nizing external (music) and internal (neuronal) rhythms. To minimize distraction by surprising elements, three complementary strategies are applied: The music is band-pass filtered to remove both ultra-low (< 45 Hz) overly loud bass and high pitch sounds (> 5 kHz) that tend to become annoying over time. The melodic structure is ex-empt from ruptures, such as pauses, breaks or major vol-ume deviations. Finally, the complexity level (e.g. type and number of instruments) remains constant through the musical piece. Amplitude modulations in the low-frequency range: A low-frequency oscillator generates amplitude modula-tions in the low-frequency range (<20 Hz). For example, beta modulations (12-18 Hz) are used to stimulate atten-tional focus, as beta-band activity is related to the mainte-nance of the current cognitive state9. Thus, entrainment of beta oscillations helps listeners keep their attentional focus for a longer time period. These amplitude modula-tions are applied to the musical piece (see Figure 3) in a selective manner to avoid habituation effects, mainly by varying the specific frequency of modulation every 30 se-conds. Additionally, the waveform and the central frequency of the inserted amplitude modulations

Figure 4. Profile of the wave used in amplitude modulations, mimicking neuronal oscillations.

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Figure 5. Traditional auditory stimulation vs. Brain.fm: Well-engineered and pre-scientifically val-idated music platform which is more efficient and en-joyable than our competitors.

are set to maximize neuronal entrainment, by mimicking the natural profile of neuronal oscillations, characterized by an irregular profile and a sharp peak10 (see Figure 4). Individually tailored and self-adaptive algorithm: Although neuronal oscillations are present in all listeners, each individual has a specific frequency profile of oscillations. Such inter-individual neu-ronal variability has been linked to inter-individual behavioral differences11, establishing a strong link between neuronal oscillations and cognitive functions. Brain.fm capitalizes on the individualized feedback of each listener to adjust the central frequency of the amplitude modulations applied, so that each listener is stimulated at its preferred frequency. Additionally, every musical element (note, drumbeat and so on) is inserted in phase with the amplitude modulations. This maximizes entrain-ment by reinforcing the rhythmic structure of the musical piece. In Brain.fm, amplitude modulations are the key element to stimulate cognitive functions, all the other parameters being organized around this dimension. Better Audio: Frontal orientation of the musical piece in the 3-D space: Finally, Brain.fm ex-ploits HRTF 3-D spatial audio techniques involving head-related transfer functions to create the subjective illusion that the musical piece – when heard on regular stereo earbuds or headphones – is played in front of the listeners. This manipulation attracts listeners’ attention on what they’re doing (which usually takes place in front of us). Additionally, the position of the musical piece slightly moves in the 3-D space over time, to reduce habituation. Competitive Advantages: Most auditory stimu-lation methods rely on already existing musical pieces (e.g. Pandora or Spotify ‘focus’ playlists), natural sounds (e.g. CalmSound), white noise, or even pure sine waves (Binaural beats). Such audi-tory signals can indeed influence listeners. For example, classical (known as the Mozart effect) or pop music can transiently improve arousal, but this effect critically depends on the enjoyment and engagement of each listener12. Critically, none of these sounds or melodies is designed à priori to target a specific cognitive state (such as focus, relax or sleep). In total, Brain.fm is the on-ly music service offering scientifically engineered tunes with the right spectrum to stimulate the brain. References 1. Salimpoor, V. N. et al. Interactions between the nucleus accumbens and auditory cortices predict music reward

value. Science 340, 216–219 (2013). 2. Buzsaki, G. Neuronal Oscillations in Cortical Networks. Science 304, 1926–1929 (2004). 3. Lee, M. H., Smyser, C. D. & Shimony, J. S. Resting-state fMRI: a review of methods and clinical applications.

AJNR Am J Neuroradiol 34, 1866–1872 (2013). 4. Siegel, M., Donner, T. H. & Engel, A. K. Spectral fingerprints of large-scale neuronal interactions. Nat Rev Neu-

rosci (2012). doi:10.1038/nrn3137 5. Schön, D. & Tillmann, B. Short- and long-term rhythmic interventions: perspectives for language rehabilitation.

Annals of the New York Academy of Sciences 1337, 32–39 (2015). 6. Jones, M. R. Time, our lost dimension: toward a new theory of perception, attention, and memory. Psychol Rev

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83, 323–355 (1976). 7. Jones, M. R., Moynihan, H., MacKenzie, N. & Puente, J. Temporal Aspects of Stimulus-Driven Attending in

Dynamic Arrays. Psychological Science 13, 313–319 (2002). 8. Lakatos, P., Karmos, G., Mehta, A. D., Ulbert, I. & Schroeder, C. E. Entrainment of Neuronal Oscillations as a

Mechanism of Attentional Selection. Science 320, 110–113 (2008). 9. Engel, A. K. & Fries, P. Beta-band oscillations—signalling the status quo? Current Opinion in Neurobiology 20,

156–165 (2010). 10. Nikolić, D., Fries, P. & Singer, W. Gamma oscillations: precise temporal coordination without a metronome.

Trends in Cognitive Sciences (2012). doi:10.1016/j.tics.2012.12.003 11. Cecere, R., Rees, G. & Romei, V. Individual Differences in Alpha Frequency Drive Crossmodal Illusory Percep-

tion. Curr. Biol. 25, 231–235 (2015). 12. Schellenberg, E. G. & Hallam, S. Music listening and cognitive abilities in 10- and 11-year-olds: the blur effect.

Annals of the New York Academy of Sciences 1060, 202–209 (2005).