DREAM DECODER

TABLE OF

CONTENTS

Sl. No TopicI AbstractII Introduction

i Struchture of Brainii Structure of Neuroniii Types of Brain Waves

III Project Descriptioni. Recording Neural Response:

ii. Fabrication of Nano-Scale Device:

iii. Imaging Neural Response:

iv. Placement of Devices:

a. ApplicationIV Conclusion & Recommendation:-

i. Scope of the Project:ii. Application in Medical Sciences:iii. Application in Physics

V References

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I. Abstract:-

The aim our project is to design a Nano based device which can decode Dream.Dreams

are basically a series of thoughts, images, and sensations occurring in a person's mind during sleep.

Dreams are successions of images, ideas, emotions, and sensations that occur involuntarily in

the mind during certain stages of sleep. Dreams may be about a cherished aspiration, ambition, or ideal, or

may be sometimes dangers, sad and bad events etc. However what are dreams or why do we dream is not

so sure to the scientists still today. The scientific study of dreams is called oneirology. In

the Greek and Roman periods, the people believed that dreams were direct messages from one and/or

multiple deities, from deceased persons, and that they predicted the future. Sigmund Freud explained

dreams as manifestations of our deepest desires and anxieties, often relating to repressed childhood

memories or obsessions. With the development of science today we know when we dream, what happens

to our body during dreaming. But it is a surprising thing that we forget most of the dreams we see just

after our awakening from sleep. We all experienced that when we dream images comes like a movie.

What if a technology can be developed that can record the movies in a similar way like a video recorder or

at least the images like a snapshot, and then watch in an output device like television or computer, that

will be better way to interpret the dream. If this kind of a thing can be developed Interpretation of Dreams

will be far easier.

We know that dreams are generated inside the brain, and from recent studies it has come to

know that during dreaming the Theta waves (frequency range – 3-8 Hz), from the brain, predominates. So

our target is to catch the theta waves and then process it to convert into image forms and if possible into

video output forms with the help of Nano-scale devices. But before that it’s important to know the basics

of Brain Structures and Structure of Neuron Cells.

II. Introduction:-

i. Structure of Brain: [2] :-The Basic parts of the brain are depicted in the below

picture:-

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ii. Structure of Neuron:- [3][5] Our brain consists of an average a total of 86 billion neurons and each

neuron has an average 7000 synaptic connections to other neurons. The brain of an adult has of the

order of 1014 synapses. Thus there exists a Neural Network within our brain and the network

reaches out to our body as well.It is through this neural network, that information is taken in via

various sensory neurons, and is then processed in our brain with the help of the neural network.

What we would essentially like to do is to record and map out he neural activity using

nanotechnology in a very accurate way. Recording the neural activity means: to be able to record the

various (electrical) signals produced by vast number of neurons and then analyse the (huge

collection of) data obtained by finding patterns in them (or anything else) and see how they corelate

to thoughts, emotions, dreams & consciousness. A cell membrane at rest has certain voltage which is

around 70 mV. This is known as the Resting Potential. It implies that the neuron at rest is electrically

charged and the voltage difference comes from the fact that the inside of the cell (intracellular) has

more negative ions compared to the outside of the cell (extracellular). When a neuron fires, it sends

out an electrical signal (via it's axon to other neurons) called action potential. An action potential is a

roughly 100 action potential mV fluctuation in the electrical potential across the cell membrane that

lasts for about 1ms. Action potential generation also depends on the recent history of cell firing. For

a few milliseconds just after an action potential has been fired, it may be virtually impossible to

initiate another spike. This is called the absolute refractory period.

iii. Types of Brain Waves: - [6] Brainwaves are produced by synchronized electrical pulses from

masses of neurons communicating with each other. Our brainwaves change according to what

we’re doing and feeling. Brainwave speed is measured in Hertz (cycles per second) and they are

dived into bands delineating slow, moderate, and fast waves. The typical Brain Waves during

different times of sleep can be presented as below:-

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As it is a proven fact that the dreams occur during light sleep or REM sleep, our aim will be to catch and

decode the Theta waves.

III. Project Description:-

i. Recording Neural Response: Here we are going illustrate how we can use nanotechnology to map

out the neural activity. Now two things have to be kept in mind while approaching this problem :

1) Signals obtained from individual neurons, i.e., probe the cellular or subcellular region with high

resolution. (tiny sensors covering the ultra microscale). 2) Signals obtained from the network in large,

which means the sensor spanning out (or should probe) a large number of neurons simultaneously,

that is covering the network at the maximum scale possible. (sensor covering the macroscale). With

nanotechnology, it is possible to create sensors which can probe into cellular scale and sub-cellular

scales. The size of a neuron or nerve cell ranges from 4 microns to 100 microns in diameter. Length of

the axon varies froman inch to several feet. Thus a nanometer sized probe can potentially probe inside

a cell membrane. Our purpose is to record this action potential from as many neurons as possible

simultaneously. Because it is these very electrical signals (action potential) which are travelling to and

fro, from one neuron to another throughout the network. And the integration of various signals in the

network give rise to the various phenomena such as thoughts, emotions, dreams and consciousness.

The basic block diagram may be developed as per the following way:-

As per the block diagram the complete procedure may be divided into following steps:-

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Collection of Brain Waves:- In this system all the kind of brain waves, generated from different

parts of the brain will be collected irrespective of their frequency & amplitude.

Store the wave:- Here a memory device will store the waves as signal forms.

O/P to a device: - The stored signals from the memory element will be collected to some device

for further processing.

Selection of required wave:- As we know during dreaming the kind of waves that dominates is

Theta waves (frequency -3-8 Hz), hence they may be sorted out with the help of some kind of

filter.

Decoding of Selected wave: The collected wave need to be decoded and converted to digital form

for further processing.

Imaging the wave: - The digitized waves or vectors will be compared to some earlier set values of

data for imaging the waves.

Now a brief procedure for collection of brain waves may be explained as follows:-

ii. Fabrication of Nanoscale Device: Here we shall illustrate 2 nanoFET based sensors which can

potentially record the active potential of a single neuron:-

KINKED NANOWIRE PROBES:[8,9] Conventional linear geometry FETs cannot probe into the sub-

cellular level efficiently. Therefore, the structure of the FET device has to be modified in order to

probe within cells. One way is to use kinked Silicon nanowires in a V or U shape and the nano FET is

introduced at the kink tip. Si NW are being chosen because very small probe devices can be created

using

structure) directly during synthesis. This allows the highly localized FET at the kink tip to be inserted

into the cell without damaging it. These devices when coated with phospholipid layers (similar in

structure to the cell membrane) penetrate the cell membrane very easily and record intracellular action

potentials [9,10].

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Synthesis : These nanowires can be synthesized using gold nanocluster catalyzed vaporliquid-solid (VLS)

growth mechanism in a (CVD) chemical vapor deposition system [11]. The voltage-sensitive active

channel : nanoFET is encoded close to the kink tip directly during synthesis. Light doping modulation is

used to encode the nanoFET channel very close to the probe tip. The arms of the nanowire are heavily

doped to serve as nanoscale source, drain electrodes. A cell probe can then be fabricated by connecting the

nanoscale S/D arms to strained microscale metal interconnects as shown :

BRANCHED INTRACELLULAR NANOTUBE FET PROBE (BIT-FET) [ 10]:

Another sensor that can be used for highly sensitive, high resolution intracellular

recording with absolute smallest probe size which can probe into sub-cellular structures : neuronal

dendrites and dendritic spines, is the following : Branched Intracellular Nanotube FET Probe (BIT-FET).

Features: This sensor consists a vertical SiO2 nanotube integrated on top of a FET channel (a Si NW

channel). When the nanotube enters the cell membrane, the cytosol fills the nanotube and activates the

FET channel which enables recording : intracellular action potentials. This nanodevice too is

functionalized with phospholipid layers which allows nanotube to enter the cell membrane with ease and

also allows a tight seal between the nanotube probe and the (interior) cell membrane [10]. Because the

probe is of ultra small size, it allows minimal invasiveness into the membrane without disrupting cell

structures. Also it allows repeated recording of potentials at the same cell site which makes it reliable for

long term, stable recordings and a robust nanosensor. It is a highly sensitive probe that can even probe into

sub-cellular structures which makes it an ideal intracellular probe for recording (neuron cell) membrane

potentials. So, the above two nanosensors can be utilized for recording intracellular cell membrane action

potentials with very high resolution. Now for implementing the Nanosensors one of the ways to do that

efficiently, is to integrate the above tiny nanosensors to a macroporous 3D electronic flexible network.

This 3D electronic network with it's large area and embedded with a very high density of nanosensors can

then be used to probe the extremely large number of neurons present in the neural network

simultaneously. Becaue the network or mesh will be porous, it will allow neurons to interpenetrate and

lead to scaled-up recordings.

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Location of FET receiver:

3D MACROPOROUS NANOELECTRONICS NETWORKS [10,11] :-

Features:- of such an electronic network/mesh. It is macroporous which enables 3D interpenetration ofcells. The structural elements have nanometere to micrometer scale dimensions comparable to biomaterialscaffolds. It has 3D interconnectivity and mechanical properties similar to natural tissue. The nanoscaleprobes within the network offer minimal perturbation to cells and tissues. The high density of nanoscaleprobes yields high spatio-temporal resolution and high density recording.

Concept of merging nanoelectronic networks with living tissues seamlessly [10,11,12] :-

* The nanoelectronic network in 2D is fabricated with underlying sacrificial layers andsubstrate support. The above mentioned nanoFET probes are incorporated in it. Thisyields a 2D electronic network with multiple nanoprobes attached to it.

* The sacrificial layers are removed to release the nanoelectronic network and yield 3D,free-standing macroporous nanoelectronic scaffolds (nanoES). The nanoES havenanometer to micrometer features with high (>99%) porosity, highly flexible andbiocompatible. These nanoES can then be used alone or combined with tissue scaffoldmaterials which makes them suitable for tissue culture

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* Cells are seeded and cultured in the nanoES to yield 3D nanoelectronic-tissue hybrids

Fabrication[11]:- a layer of negative resist (SU-8) is coated on a nickel sacrificial layer, a solution with

kinked nanowires are deposited onto the SU-8 layer and allowed to evaporate, and then SU-8 is patterned

by lithography to immobilize nanowires and to provide the basic framework for nanoES. Extra nanowires

are washed away during the development process of the SU-8 structure. Metal contacts are patterned by

lithography and deposition. Finally, a layer of SU-8 is deposited and lithographically defined as the upper

passivation layer on the interconnects.Two nanoES were designed using the above concept and fabrication

method. The one we are going to discuss about is Reticular nanoES [10,11]. These are made by electron

beam lithography (EBL). The metal interconnects in the 2D structure are stressed (purposely designed this

way) such that on removal of the sacrificial layer, it self-organizes into a 3D structure. The 3D structure

resembles the fibrous meshwork of the brain tissue [10,11].

iii. Imaging of Neural Response: - [7] A Machine learning based method is chosen to accomplish the task

of prediction in this application. The motivation is that the highly stochastic uncertainty of the signal

received makes deterministic decision making erroneous. Also the noise corrupting the signal which is

picked up by the sensors has to be taken care of in the analysis part.

Denoising Module:- Firstly the recorder signal is denoised to make the SNR of the signal high. Our

assumption here is the noise associated are mainly high frequency noise. So a low pass filter will

serve the purpose here. This also prevents aliasing. The cut off frequency is selected quiet

methodically so that information is not lost.

Training Module:In the training phase we need to collect a reliable dataset. Experiments are

conducted in a controlled environment where subject is simulated with a particular genre of dream and

the response is recorded. This data is denoised as described the above module and standard datasets

for training a learning algorithm is generated. As the problem at hard deals with learning a highly non-

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linear response we select a strong classifier like Random Forests or Multi class SVM with Gaussian

kernels. The Classifier is trained with the generated dataset. The number of data should be very large

to make the model robust and generalized and to prevent over fitting.

Cross Validation Module: The Training Dataset is divided into 2 part 80% and 20%. The training is

accomplished in 80% and validated in 20%. This is to validate whether the training model is robust

enough or not. This is done in an iterative fashion until the error is very less.

Performance Analysis Module:- The performance of the learning model is evaluated by testing in a

new testing datasets. Its Receiver Operating Characteristics (ROC) is plotted and the Area under the

Curve (AUC) is evaluated to see the genetralizability and sensitivity of the learnt model. If all the

parameters are good enough it is deployed at the end of the proposed framework for reliable dream

decoding.

iv. Placement of the Device:- Theta waves occur mainly in the parietal and temporal part of the

cerebrum, and hence may be collected from a bunch of neuron situated in this region. Here we

propose a method, in which the nano-scale FET is placed inside the brain & the signals collected are

stored in a method as suggested before. From the memory element or recording device the data is

transferred with the help of Carbon Nano wires, which may be regarded as an ideal conductor whose

conductivity is pretty much higher than copper, to the Receiver/decoder where the data is analyzed

and the images are formed. The placement of the devices may be as shown in the figure below:-

There are two possible ways of implanting the electronic network within the human brain which (in the

near future) can enable extremely efficient, high resolution brain wave recording :

* First the flexible nanoelectronic network device can be transferred to the surface of a

biodegradable polymer layer and then through direct mechanical insertion as is used with

conventional neural probes is carried out. Inside the brain, the sacrificial support dissolves and the

nanoelectronic device network remains inside the brain giving a seamless nanoelectronics/neural

network interface [12]

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* The highly-flexible nanoele ctronic network is injected via a syringe needle directly into the brain

Post-injection, the nanoelectronic network is expected to yield a seamless nanoelectronics/neural

network interface [12].

v. Application:-

IV. Conclusion & Recommendation:-

By looking into the dream subjects analysis, each neuron cell provokes thoughts,feelings and abstract ideas. 200 billion tiny neurons exists within a world of possibilities with a complex traffic of signals and mechanical like system. The answers to why we dream and the genesis of our neuron networks lies within the logical and imagination input of fet sensors. The fet sensors carry little risk to the test subjects within the implementation process. Recordings of dreams might even be applied to determining the personality traits of the dreamer and training in spatial tasks if dreams can be some how manipulated to produce training sessions of a learning task such as :Math,cooking, engineering,etc.

It will break the boundaries of the human mechanical learning process and therefore possibly produce a far more advanced civilization mankind can ever imagine! No child will be left behind and the doorsof opportunities will open to science into understanding the unknown of the universe and challenges we face everyday. In conclusion , The nanotech Fet sensors implementation for decoding dreams would pave the way for treating cerebral ailments and give mankind a fantastic future.

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V. References:-

1. Wikipedia, www.google.com

2. Source: - Wikipedia.

3. A course on Foundational Neuroscience for Perception and Action, Leonard E. White, PhD, Duke

University

4. A course on Computational Neuroscience, Rajesh P.N.Rao & Adrienne Fairhall, University of

Washington, Seattle, USA

5. Theoretical Neuroscience, Computational & Mathematical Model of Neuron System, Peter Dayan and

L.F. Abbott.

6. Journal: Type of Brain waves, EEG sensor and brain wave – UK,

7. Abhijit Guha Roy, 2nd Year M Tech, Instrumentation and Signal Processing, IIT Kharagpur .

8. Chapter 6 : Nanowire-based Sensors from the book : 'Nanotechnology and nanosensors' by Prof.

Hossam Haick.

9. X. Duan and C.M. Lieber, “Nanoelectronics meets biology: From new nanoscale devices for live-cell

recording to 3D innervated tissues,” Chem. Asian J. 8, 2304-2314 (2013).

10. P. Kruskal, Z. Jiang, T. Gao and C.M. Lieber, “Beyond the patch clamp: Nanotechnologies for

intracellular recording,” Neuron 86, 21-24 (2015).

11. B. Tian, J. Liu, T. Dvir, L. Jin, J.H. Tsui, Q. Qing, Z. Suo, R. Langer, D.S. Kohane and C.M. Lieber,

“Macroporous nanowire nanoelectronic scaffolds for synthetic tissues,” Nature Mater. 11, 986-994

(2012).

12. X. Duan and C.M. Lieber, “Nanoscience and the nano-bioelectronics frontier,” Nano Research DOI:10.1007/s12274-014-0692-8, 10 Dec 2014.

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Biography of the Students:-

Alan Perez- Daytona state college ,completing AS computer science and in processes certification for

pentesting.

Jaydeep Bhadra:- Completed Bachelor of Electrical Engineering from Jadavpur University in the

year 2012. Presently working in a public sector organization named Damodar Valley Corporation,

West Bengal, India. Email id- jaydeep241090@gmail.com/bhadra.jaydeep.dvc@gmail.com.

Depika Roy

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