By Jordan Sorkin
Q1: I'm an Italian Physician Master in Clinical Neurophysiology and Master in Fitotherapy. I'd like to study the brain waves. What links and books do you suggest for my research?
Q2: I am an electrical-mechanical engineer and I am very interested in virtual reality. I would like to do this without movement but with the use of brain waves. I know little about neuroscience and my question is where can/should I start my journey into this field?
Ah, for the love of brain waves. Yes, these flowing electrical fields have been of great interest to physicians and scientists since their discovery in animals in 1875 and humans in 1929 (Haas 2003). Instead of merely listing off a few resources on brain waves, I think it will be helpful to introduce them in a historical and scientific context.
To start, these rhythms were quite the enigma following their discovery, as their cellular basis was unknown. In fact, the first person to record brain waves, Hans Berger was so unsure of his result that it took him 5 years to publish his findings, which remained controversial for more than a decade afterwards (see timeline in the inset). However, by 1938, Berger’s results had been accepted by the international scientific community, and the EEG had become a key diagnostic and research tool. For instance, physicians used the EEG to facilitate their diagnosis and characterization of schizophrenic and epileptic brain abnormalities. At the same time, scientists realized the potential of the EEG as a research tool. Early studies investigated the meaning behind event-related potentials (ERPs), small deflections in the EEG that occur within a few hundred milliseconds following an auditory, visual, or motor event (Luck 2005; Hansen and Hillyard 1980). Other studies investigated cognitive processes, such as arousal and sleep, uncovering links to the synchronization and desynchronization of brain rhythms (Pfurtscheller and Aranibar 1977; Agnew, Webb, and Williams 1966).
1925: Berger invents the encephalographam (EEG), and records electrical activity from the human brain.
1929: Berger publishes his paper, which is met with … skepticism.
1934: Edgar Douglas Adrian and B.H.C Matthews publish confirmation of Berger’s results
1937: Berger gets international credit for his work on EEG
1938: EEG has gained widespread acceptance by scientists, and has been adopted for practical diagnostic use.
[See resources section at the end of this post for full citations]
EEG and Schizophrenia: Spencer et al. 2003; Haenschel and Linden 2011
EEG and Epilepsy: Jasper and Nichols 1938; Walter 1939; Margerison and Corsellis 1966
EEG and Psychological Research: Agnew, Webb, and Williams 1966; Pfurtscheller and Aranibar 1977; Luck 2005; Williams 1939; Woods and Clayworth 1987; Hansen and Hillyard 1980
However, increase in the amount of information that scientists and physicians were able to acquire from the human brain came with its own obstacles. Notably, EEG-recorded brain activity is highly non-stationary, meaning the probability distribution of the data points collected from the EEG shift over time (see Fig 1). This makes traditional time-domain analysis of raw brain waves challenging. One alternative – and hugely important – solution to this predicament was first implemented in the 1930’s: using the Fourier transformation (FT) to deconstruct raw EEG recordings into the various frequencies that, when appropriately combined, reconstruct the original data (Grass and Gibbs 1938). By applying FTs on EEG recordings, researchers were able to pinpoint various physiologically relevant frequencies. Moreover, the FT is the basis of more modern EEG analyses such as wavelet transformations, feature extraction for EEG classification, and phase coherence (for synchronicity measurements), among others.
Figure 1: EEG is non-stationary Top: simulation of 8 seconds of EEG data. Multiple frequencies contribute to the raw signal to varying degrees, which change over time. Bottom: normalized histograms of the EEG over 2-second windows. The probability distribution of the signal changes over time. From left to right: 0-2 seconds, 2-4 seconds, 4-6 seconds, 6-8 seconds.
Fourier Transform: Knott, Gibbs, and Henry 1942; Hord et al. 1965; Paranjape, Koles, and Lind 1990; Kawabata 1973
Wavelet analysis: Bosnyakova et al. 2006; Torrence and Compo 1998; Van Luijtelaar et al. 2011; Ovchinnikov et al. 2010
EEG classification: Übeyli 2008; Lotte et al. 2007; Garrett et al. 2003; Murugappan 2010
Phase Coherence: Tsai et al. 2010; Achermann and Borbély 1998; Spencer et al. 2003
In addition to these new techniques to analyze EEG recordings, new findings regarding the neural sources of brain waves began emerging by the 1960’s. Experiments began to uncouple the relationship between the activity of populations of neurons – the primary information-processing cells of the brain – and brain rhythms. In particular, the frequency and amplitude of the EEG seemed to correlate with the rate of activity of individual neurons, both of which slowed down during sleep (Evarts 1962; Evarts 1964; Green et al. 1960). Moreover, bursts of activity of neurons correlated with EEG “spikes”, high-amplitude deflections, during a seizure(Enomoto and Ajmone-Marsan 1959). We now know that EEG actually reflects the currents induced by multiple simultaneous inputs onto cortical neurons closest to the EEG electrodes themselves.
Neural Spikes and EEG: Enomoto and Ajmone-Marsan 1959; Evarts 1962; Evarts 1964; Green et al. 1960; Steriade 2000; Contreras and Steriade 1995
Basis of EEG: Lopes Da Silva and Storm Van Leeuwen 1977; Lopes da Silva 1991; Schaul 1998
In recent years, advances in both hardware/software and our understanding of the neural underpinnings of cognition have facilitated the use of brain-computer interfaces (BCI), machines that directly record and interpret brain activity to perform some action. For instance, BCI researchers have improved and used these techniques to allow subjects to control the movement of computer mice by thinking about moving the mice. The general flow that this technique uses is: (1) record neural activity (EEG, single-neuron activity, etc.), (2) decode the neural activity (sort, classify, extract features, etc.), (3) use a mathematical framework to interpret the decoded activity, and (4) convert the output of this model into machine-interpretable code. Impressively, even the local friend potential (a signal similar to EEG) can be decoded into meaningful outputs (Stavisky et al. 2015).
Hopefully this rather brief exposé on brain waves has been informative. Even after 100+ years of research, we still have much to understand regarding the sources, functions, and uses of these rhythms, which makes researching them all the more exciting!
Brain-Computer Interface: Kao et al. 2014; Patil and Turner 2008; Stavisky et al. 2015; Sussillo et al. 2012; Andersen, Musallam, and Pesaran 2004; Sanchez and Principe 2007
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