EEG experiment sessions

 

The first step of the research is to propose a methodology that is fit for an event-related EEG paradigm, the main goal to collect the brain processes which are triggered by external stimuli. The challenge is to identify the variables. To get these correctly we have to answer the following questions. What will be observed and measured during the research? Dependent Variable is something that might be affected by the change in the independent variable. What are we testing or manipulating? Independent Variable – something that is changed by the researcher. EEG has very high time resolution in real time to capture event related sensory processes in the time frame in which cognition occurs. The time frame is identified in 1 min for each stimulus and the following mins is a combination of these three sensory impacts as the following timetable shows. The session is split into two main parts (Figure 1.) this is an Open and Closed eye sensory experimentation. With this method, we can identify and remove the eye-movement artifact from the electro-encephalogram as described. This is based on complex regression analysis. The experimental session intends to measure two (2) dependent variables that is representing the relaxation states in this case. A person who takes time out to reflect or meditate is usually in an alpha state. A person who takes a break from a conference and walks in the garden is often in an alpha state. Based on this fact the methodology assumes that alpha waves represent relaxation. To make sure that this state turning to meditation the software also observes theta brainwaves too. These are typically of even greater amplitude and slower frequency. This frequency range is normally between 5 and 8 cycles a second. A person who has taken time off from a task and begins to meditate is often in a theta brainwave state. Once we have identified the independent and dependent variables, we can build a timeline to categorize them. The research followed a common-sense methodology that is a statistical qualitative analysis. The following image (Figure 2.) illustrates the session storyboard in a simple way. The expected duration of the session is 16mins where the participant is sitting in a silent, dark room front of a HD 55 inches display with 1- meter viewing distance.

 

Understanding Alpha/Theta waves

 

Our brain waves change according to what we are doing and feeling. Alpha (8Hz–14Hz) is dominant during quietly flowing thoughts, daydream or during light meditation. Theta (4Hz–8Hz) is slower and more experienced in a deep transcendental meditation. These are associated with relaxation, creativity and emergent imagery that led some to speculate this. Alpha-Theta training might promote insight and be a useful method for augmenting psychotherapy. These Alpha/Theta waves also depend on environments such as light conditions and eye conditions. The following section is going to identify these effects.

 

 Event related potential under the session

 

An event-related potential (ERP) is the measured brain response that is the direct result of a specific sensory event. More formally, it is any stereotyped electrophysiological response to a stimulus. The study of the brain in this way provides a non-invasive means of evaluating brain functioning. While there is

 

visual content happening on screen and we are trying to get you to pay attention to that, your brains also doing other things. It’s processing sounds, sense, the pressure of the chair, your back and your brain may be wondering about past or future, so all that kind of activity is interfering with the many others at same time. Some way we have to reducing that background noise so the best method is use event related potential (ERP). We are take slices of that raw EEG where we (the experimenters) have somehow controlled the situation and recall your attention to a specific thing that happens plenty of times under the session. We can play a tone or a created frequency again and watch what brain does in response to that specific tone and filter out the noise from it. To make sure the quantitative EEG is the right method to get statistical evaluation I established a pilot test with few volunteers. The aim of this experiment was to examine how useful the method to analyse the usability of quantitative EEG (qEEG) applying the statistical pattern recognition method. The participants were recruited from a London based meditation group: 5 selected person [mean age 40.years , 85% women]. The software was collected the small number of samples then it was calculated an average and total summary. The conclusion that this qEEG using an event-related sensory 1mins interval timeframe over the session could be a useful method to identify the effect on theta/alpha coherence during closed eye / open-eye mindful meditation. The EEG data in the experimental group was recorded by MUSE EEG headband (version MU2 2016) with research pre-set AD 500 Hz sampling rate. (see http://developer.choosemuse.com/hardware-firmware/hardware- specifications for full technical specifications). I previously contacted the Interaxon Company to make sure that their BCI device capable for research purpose. The Muse( BCI device) has been tested against industry standard EEG systems including the Brain Vision actiCHamp system and the g.Tec g.USBamp system. Muse achieves comparable performance in voltage trace comparisons and in patterns of total and hemispheric power. The MUSE EEG system has electrodes located analogous to Fpz, AF7, AF8, TP9, and TP10 with electrode Fpz utilized as the reference electrode. Using the muse- Direct SDK on Windows 10 to streamed data from the MUSE EEG system directly to Neuro Visual software via the open sound control (OSC) protocol and later visualize in MATLAB to demonstrate in 3D brain model. I simply recorded EEG data with Neuro Visual software pre-set with 1min time segment of EEG data that were streamed from the MUSE to a laptop computer after each 60 seconds. After each interval a line is written in a CSV file with the averages and saved in a dedicated folder.

 

 Software tools under experiments

 

  • Neuro Visual – Multipurpose Neurofeedback Software ( I am collaborating as a software developer in UI-UX design and Software
  • Matlab + Brainstorm – 2D / 3D brain mapping visualisation – A MATLAB Based, Open- Source Application for advanced MEG/EEG Data Processing and
  • Muse Direct – OSC data stream – This application is a gateway tool for researchers, easy stream and save raw EEG signal to another standalone application like our developed Neuro Visual neurofeedback software. The system can gather brain data in real time connecting via a low energy
  • Microsoft excel – Creating of Statistical Diagrams – Graphs and charts are great because they communicate information visually. For this reason, graphs are the best to demonstrate the Research outcomes

 

 Participants

 

 

Postgraduate students and Academic staff from the University of West London participated in the experiment using the MUSE portable EEG system. For comparison purposes, we randomly selected 20 participants (Standard group mean age: 31years) from the University of West London. The session was placed in our Quiet Study Room 1. (PE.02.009) during the summer. Participants in this group had performed the same two experimental tasks with Open and Closed eyes in 2 x 8 mins duration. Their EEG data was collected using the Neuro Visual Software with a 7 electrode + 1 reference configuration and a typical EEG set-up for ERP experiment. Included a stimulus machine as smell diffuser, video display and a wireless recording machine. Before the session has been confirmed that all participants had normal vision with a good mental condition and no irritation or allergic reactions of essential oils.

 

 

 

Classification of EEG signals

 

The system interface is designed to translate a subject’s intention or mind into a control signal for end device. There are several practices to identify a pattern in the brain region. In this case available seven

(7) sensors were placed. Two (5) in a frontal lobe area another two (2) in the visual cortex area. Pattern recognition is an important step in the process of EEG signal classification. Doctors are using an approach that discriminates EEG signals recorded during different cognitive conditions. The process is similar to this. There are many algorithms in BCI classification systems that can identify and discriminates EEG signal pattern during the different type of meditation.

 

Linear classifiers,

Nonlinear Bayesian classifiers, Nearest neighbour classifiers, Neural networks,

Combination of classifiers

 

 

 

 Examination of Research Question 2

 

  • How can ERP / EEG research results be applied to creating multimodal neurofeedback prototype?

 

 

 Brain Activity Patterns

 

In past years statistical pattern recognition has been used in the classification of evoked potential waveforms. During the pilot EEG research, I identified five brain patterns that related cognitive and behavioural states. As neuroscientists call them neural oscillations. The challenge to making strides to link brainwaves to things like consciousness, memory, and maybe even certain diseases. There are multiple electrical patterns, defined by their frequency. They are measured in cycles, or the number of

 

times the neurons are firing, per second. Brainwaves also vary in amplitude, with lower amplitudes as they speed up. There are 5 main types as I mention before and there are no hard and fast rules about their functions. As I can confirm the higher frequency the wave the more alert and awake we are.

 

  • Delta

The slowest of these 5 waves, which are of relatively high amplitude, are delta waves. They are typically linked with deep sleep in a normal case. DELTA: ≈ 0.1-4hz. A personal note that during the EEG experiment some noise in this frequency range because the EEG also records electrical activities arising from sites other than the brain. It was clear to see the relation of eye blinks and eye‐movement artifacts in the context of an EEG dominated by delta waves.

 

  • Theta

 

Slightly faster are theta waves, which are often associated with daydreaming or meditation. THETA: ≈ 4- 8hz. These waves are connected to us experiencing and feeling deep and raw emotions. Another personal note that these delta waves are highly connecting to alpha waves during meditation practices.

 

  • Alpha

The next step up are Alpha waves. These are common when you are awake, but relaxed, like when you still have your eyes closed. ALPHA: ≈ 8-12Hz. Thinking of something peaceful (nature related) or experiencing a pleasant smell; sound should give an increase of alpha domain. Most profound in the back of the head and in the frontal area. These waves can symbolize the first stage of TM meditation state too.

 

  • Beta

Beta waves are even higher frequency and lower amplitude and seem to happen when we are awake and thinking about something. These fully awake and alert state associated with left-brain thinking activity and conscious mind. Our brain usually operates at beta waves state when any activities required a basic level of concentration or acute visual perception. BETA: ≈ 12- 30hz

 

  • Gamma

The Smallest, fastest oscillations are gamma waves. They tend to be around when you are deeply focused on something GAMMA: ≈ 30- 100 Hz. After some measurement, I faced with some muscle related artifact that occurs when the person is swallowing, grimacing, chewing. This shows a low-amplitude spike signal in the gamma range

 

 

 EEG Research results

 

 

  • Does nature-based 3 sensory stimuli significantly increase theta/alpha coherence domain of human brain waves during open-eye meditations?

The EEG pilot research demonstrates that it is possible to quantify the relaxation states and compare the open/closed eyes states. The research was conducted with a Neurofeedback software called NeuroVisual©. This software was fully compatible with the (Muse) BCI headband. The experiments were completed in under 16 mins. As it is a quantitative research it had to be plotted on a statistical diagram. The diagrams are representing how the Alpha/ Theta domain (dependent variables) was changing upon nature-related sensory stimuli (independent variables). For the Theta/ Alpha power analysis, at different brain areas, such as frontal, temporal, electrodes were placed. The investigated relaxed condition were conducted with nature sounds and city noises stimulus with varying eyes states, i. e. eyes-open or eyes- closed. After ten (10) experiments a larger theta power was found in the frontal lobe with condition (eyes- closed state than eyes-open state) and significantly larger alpha power in the visual cortex area when listening to nature-related sounds than to disturbing city noises. The key finding is that the nature-related

 

stimulus can produce vivid imagery in the visual brain area. In terms of RQ 1, the statistical result clearly shows that this open-eye meditation with nature sound stimulation does not increase Theta coherence domain of human brain waves. The hypothesis cannot be answered in this way because these nature-based 3 sensory stimuli can only increase relaxation state when it is connected to a positive memory from the past. In other cases these stimuli are neutral for the observer. Another finding is the nature-based media multi-sensory content can increase low amplitude of Alpha upon open eyes. In terms of creating a multimodal neurofeedback platform it is complex because the eyes movements and other environmental conditions cause noises in EEG signals. Further research is needed to apply Theta/Alpha brainwaves to control media contents under meditation sessions. Before the implementation, we need to conduct a classification of the EEG signals to use Delta (low frequencies) and Gamma (high frequencies).

 

 

 Diagrams

 

The following Brain map shows the Alpha/Theta amplitudes with closed and open eyes states. The Red coloured zone indicates a more relaxed state.

 

 

Statistical diagram of average brainwaves upon stimuli (sessions of 20 individuals)

 

 

An additional outcome of the research that may be interesting is that the actual environment is very important (Figure 22.) to practice efficient meditation. The best condition is with closed eyes in a dark room focusing on nature stimuli such as sounds, smells, colours and/or the combination of these. As observations show if the participant remembers something less emotional than neutral to brain coherences but if it is connected to positive emotional memories than the Alpha domain increases.

 

Efficieny of Meditation practice upon The enviromental stimuli

Credit :  Peter Varga

 

 

 

 

Proof of concept – Vivid imaginary 

 

Examination of Research Question:

 

Does nature-based 3 sensory stimuli significantly increase vivid imaginary after closing eyes?

 

The aim was with this sensory meditation pilot test is to help beginners users to stay in present moment for 10-15 minutes and experience those nature stimuli through the media software and the given aroma diffuser.

 

In conclusion, it is our belief that sensory meditation practice supported with smell stimuli are capable to extend vivid imagery and helps stay in present in a particular way. To sum up, the conducted experimental research outcomes indicate the potential benefits of sensory meditation. In summary, We can say that the nature-based sensory stimuli definitely able to increase alpha coherence domain of human brain waves during open or closed-eye meditations.

 

 

 

Concept of mindfulness-based stress reduction

using of Nature’s Stimuli 

The following diagram represents the stages of stress reduction during Sensory mindfulness meditation. Set up your own little meditation retreat in your own home or office—a small corner that promotes peace, harmony and comfort. The noise and light pollution can negatively impact this meditation in significant ways. The best environment is quiet and dark, the only light source is the screen in front of you. Another important aspect to make sure that your device conforms to the minimum technical requirements.

 

 

Credit: Peter Varga and Sandor Varga (2018)