Speak Your Mind: Extracting Imagined Words and Lettersfrom EEG Signals: Midterm Update

Yuwei HuUniversity of ConnecticutDepartment of ComputerScience and Engineering

Storrs, Connecticut

Siming LiUniversity of ConnecticutDepartment of ComputerScience and Engineering

Storrs, Connecticut

Narayana ChikkalaUniversity of ConnecticutDepartment of ComputerScience and Engineering

Storrs, Connecticut

Yue ZhaoUniversity of ConnecticutDepartment of ComputerScience and Engineering

Storrs, Connecticut

Aaron PalmerUniversity of ConnecticutDepartment of ComputerScience and Engineering

Storrs, Connecticut

1. INTRODUCTIONWith rapid advances happening in biotech, what seemed likescience fiction yesterday is reality today. One such conceptis telepathy. The notion that one can communicate withanother through nothing more than pure thought would beastonishing, with huge medical consequences. Perhaps peo-ple with locked-in syndrome or amyotrophic lateral sclerosis(ALS) who have no means of producing speech using limbor facial movements [1], like Prof. Steven Hawking, wouldbe able to ”speak” with ease once again. To see if ”synthetic”(think through a speaker) telepathy is possible, we attemptto decipher thought words from a consumer-oriented wear-able electroencephalogram (EEG) signals. EEG is one tech-nique to measure the electric fields produced by the brain.While there has been some headway in this area [4, 5, 6, 7,12], to our knowledge there hasn’t been any established base-line. We believe that by using several different approachespresented in previous works along with updated modelingtechniques we can make additional progress while simulta-neously setting up a standard repository.

For the experiment, a generalized randomized design is usedto collect data where each subject speaks a word in theirhead (like silent reading), while EEG data is recorded from14 sensors placed over the scalp with the EPOC+; a $400device from Emotiv [2] sampling at about 127.89 Hz. Whilethere are devices substantially more expensive with resolu-tion orders of magnitude stronger, that would defeat thepurpose of using a consumer oriented product. We cannotoverlook the possibility that what we are trying to accom-plish just may not be possible with the device we plan on us-

ing. Figure 1 contains two images. One is of the traditionalEEG machine, and the other is using Emotiv EPOC+.

Figure 1: Traditional EEG Collection on left, Emo-tiv EPOC+ on right*

*Image from Emotiv website

EEG data is a high dimensional time series which requirescareful preprocessing and data munging before features canbe extracted for classification. Utilizing both classical andstate of the art classification algorithms, we hope to breaknew ground in this ongoing study. The goal is to ultimatelyhave a user think the words of a conversation while the EEGsignals are decoded with synthesized speech coming out ofa speaker in real time.

This project proceeded in several stages with every piecestrongly relying on the assumption that the previous stepwas performed adequately. The initial assumption, previ-ously mentioned, is that this task is possible. If indeed itis possible, then we must assume the chosen device is ca-pable of detecting the signal. With these two fundamen-tal assumptions met, the project then hinges on a properlydesigned experiment to collect data. If we have sufficientconfidence that quality data has been collected, (hopefully)containing a sufficiently discriminative signal hidden insidethe noise, we then proceed to preprocessing the data. Thisstage will consists of filtering the tremendous amount ofdata. Only then can we hope to create strong features andbegin classification testing. Feasibility experimenting will bedone offline, with the ultimate test of an online system ca-pable of forming a complete sentence from imagined words.

2. RELATED WORKLuckily we had some precedent to think this a viable project.In a review of the current literature, Han-Jeong Hwang etal [3] bring to light the success that has happened in brain-computer interfaces (BCI). They describe feature extractionfrom the acquired EEG signal such as power spectral den-sity (PSD), event-related potential (ERP), phase informa-tion among others. With features extracted they continueon about the success different classification algorithms havehad in the EEG-based BCI articles surveyed. These includedlinear discriminant analysis (LDA), support vector machines(SVM), Bayesian classifiers, neural networks (NN) to namea few.

With guidelines in hand for general EEG related BCI taskswe sought to find more related to the specific task of us-ing imagined words to communicate. This topic has beenexplored by several researchers and we will briefly touchupon their experiences and what we can use. Research byBrigham and Kumar [4] created an experiment where 6 vol-unteers imagined speaking two different syllables, /ba/ and/ku/. These syllables were chosen so that they didn’t haveany semantic meaning. After going through several prepro-cessing stages, including artifact removal (e.g. eye move-ment) and noise reduction (removal of line noise), they ex-tracted features which in this case were AR coefficients. Inorder to classify a given signal into /ba/ or /ku/ they useda 3-Nearest Neighbors classifier based on the Euclidean dis-tance between AR model coefficients in the training and testset [4]. While this paper had, what seemed to be reasonablepreprocessing, it seemed to lack on feature extraction andclassification.

Brigham and Kumar built on work done by D’Zmura et al[6]. In addition to having a similar experimental design,D’Zmura et al [6] included many offline preprocessing stepsalong with extraction of spectral features.

One paper in particular looked quite promising for prepro-cessing was written by Jung et al [9]. In it the group de-scribes how using independent component analysis (ICA)can greatly enhance artifact removal. While their paper fo-cused on event-related potential (ERP) experiments, one canthink that an imagined word could similarly be viewed asan event, and with similar preprocessing will lead to datawith higher signal-to-noise. Makeig et al [10] continue totalk about the advantages of ICA in relation to single trialdata. However, for the time being single trial experimentsare still unrealistic.

In a different paper Oshri et al [5] used pairs of imaginedsyllables /ba/ and /ku/ along with /im/ and /sa/. Theyappeared to have a similar experimental design to Brighamand Kumar [4] but extracted other features and extended themodels used for classification. The most promising resultscame from using artificial neural networks with the reasonlikely being the ability of the hidden units to model the highnonlinearity of the data.

Surprisingly a paper by Suppes et al [12] presents exactlywhat we hoped to accomplish. The words they chose couldnot form a sentence, and data was collected from a devicethat would definitely not be considered a wearable. We will

use their work to offer insight, however since the paper isfairly old we will be able to implement more sophisticatedprocessing and classification algorithms.

Our approach will seek to keep what works from prior re-search and take the next step; namely begin to classify fullwords, if not sentences, with reasonable accuracy with adevice much cheaper and less sophisticated than our prede-cessors.

3. EXPERIMENTSince the EPOC+ is an inexpensive (relatively speaking)EEG we must take into account the possibility that it doesnot collect great quality data. With the limited number ofchannels and substantially smaller sampling frequency thedesign of the experiment is of critical importance.

As far as the subjects are concerned we wanted to have anequal amount of native vs non-native speakers, however dueto recording and noise constraints we only managed threesubjects. It seems reasonable that non-native speakers maynot immediately think the word in English, but rather theword is subconsciously translated. Should this be the caseperhaps this will offer insight down the line. However, inthe hopes of being able to combine all data to create a sin-gle model we decided to look at language background asa blocking factor in a generalized randomized block designwith words as the treatment. This way the block factor willbe eliminated and the treatment of interest (words) couldhave a better inference outcome.

Where prior work focused on using only two monosyllabicsounds with no semantic meaning associated with them [4,5, 6] as the classes, we opted to use real words. However, thewords we chose were still monosyllabic. Additionally, theywere chosen such that several parts of speech were repre-sented. There are two nouns, adverbs, adjectives, and verbspresent with each of the words chosen to have a differentstarting letter. Regarding the parts of speech, we hypoth-esize that different parts of speech elicit stronger signals,for example ”dog” may provide a stronger signal than ”the.”Moreover, by choosing each word to have a different firstletter we hope this offers additional signal discrimination.

Previous designs focused on using a stimulus to prompt theuser [4, 6] to imagine speaking a word, and we followed suit.Even though the papers didn’t explain their reasoning, wesupposed it came from a tradeoff of signal and noise. Anystimulus naturally will obscure the signal, yet it is a ratio-nal price to pay to ensure the data is collected on specifiedintervals; especially since we will present randomized words.

Before the experiment begins we informed the subject thata series of randomized words will appear on screen for a mo-ment, 2 seconds, and they should silently say the word intheir mind. After 2 seconds the screen will go blank for ashort washout period, 2 seconds, with the next word appear-ing. A diagram of a partial trial seen in Figure 2.

Since there are only 8 words in the data set, we repeat eachword 5 times inside each trial with 15 trials per subject. Ouraim is to collect each word in total 75 times. We chose thesenumbers doing our best to weigh the tradeoff of trial length

Figure 2: Experiment Setup

and number of trials vs fatigue and disinterest. Collectingpoor data would be just as bad as noisy data.

3.1 Data CollectionFor the collection and analysis of the data we have chosen touse Python [13] as it can tackle the entire project flow fromdata acquisition to preprocessing and classification. We ini-tially tried to use Matlab, but there were bad dll files offeredby the company along with terrible customer support. Afterswitching to Python, and after swapping the appropriate dllto accommodate Windows 64 bit, we had our first taste ofsuccess.

A proctor was be present to ensure the sensors were makingadequare contact with the subject’s scalp before and duringthe experiment. They will monitor this through the Emotiv[2] SDK Testbench software. Figure 2 is a small diagramshowing this. The sensors on the scalp will need to be yellowor green to ensure minumum quality.

Figure 3: Proctor Monitoring Page

The data transmitted by the EPOC+ via Bluetooth is cap-tured by its dongle once the Python script is run with eachsample being timestamped by a crystal clock located on theheadset at about 127.89 Hz. Upon trial completion the datais then written to a csv file, while another csv file with theword order and its timestamp is written out. Below are twoimages illustrating a small sample of collected output.

In Figure 4 we can see data for the last 2 sensors, the gyro-scopic X and Y values along with the timestamp. In Figure5 we can see the word along with the timestamp of its onscreen appearance.

In Figure 6 each vertical line demarcates the occurrence ofeach word. This is a real trial with all 14 time series, one

Figure 4: Sample of recorded EEG sensor data

Figure 5: Appearance of word with timestamp

for each of the sensors located on the scalp. The left columnof plots are from sensors located on the left hemisphere ofthe brain, with the right column showing those of the righthemisphere.

4. METHODS4.1 PreprocessingOnce data collection was completed, there were several stagesof preprocessing to do. This is a crucial step as EEG data isincredibly noisy. It contains artifacts like eye blinking, andmuscle movement which obscures the signal. After speak-ing with Professor Santiello Sabato in UCONN’s biomedicalengineering department we proceeded with the following.

4.1.1 Montage AveragingA relatively simple technique, montage averaging removesthe average of all sensors from each raw sensor data. It isdefined below.

ti,j,k = rawi,j,k +

14∑i=1

rawi,j,k

14∀j, k (1)

Here ti,j,k represents the data for the ith sensor for the jth

subject for the kth trial. The new t′i,j,k is then standardizedusing equation 2 with i, j, k representing the same quantitiesas before.

t′i,j,k =(ti,j,k − µ(ti,j,k))

σ(ti,j,k)∀j, k (2)

Brigham and Kumar [4] used Hurst exponent, which mea-sures the long-term memory in a time series, to filter theEEG data discarding anything outside the range of 0.70 -

Figure 6: Single Trial of real data with each timeseries corresponding to a single sensor. The left col-umn is sensor data from the left side of the brain,while the right column is data from the right side ofthe brain

0.76 after using Independent Component Analysis (ICA) tofind sources that are statistically independent. Additionally,they opted to discard the entire trial if no independent com-ponents have Hurst exponents values within the predefinedrange. This range was chosen because it was deemed to havevalue by Brigham and Kumar. Based on other resources weopted to use the Hurst exponent as a feature instead of forfiltering. However, future research and work may having usincluding it here along with ICA.

After montaging, and standardizing we took the discretewavelet transformation (DWT) which can help in additionalde-noising and compression of signals. This is an alternativeto using the discrete Fourier transform (DFT) as waveletswill include both time and frequency information. We optedto use the Daubechies, db4, wavelet for this project as itwas used by Adeli et al [14] in their classification of epilep-tic fits. However, we can’t overstate enough that we moreor less chose this arbitrarily and would need a domain ex-pert or very good cross validation to inform of an appro-priate wavelet. Additionally, the approximation level usedwas chosen based on visual inspection. This would NOT bethe way to do it, but again with little domain knowledgeamongst our group we opted to try it rather than not. Atworst, perhaps would attenuate noise along with signal toomuch.

Figure 7: Four DB4 level approximations for a sam-ple EEG signal from the AF3 sensor

4.2 Feature ExtractionThe types of features we look to extract will depend on thekinds of models we will use later in classification. Becausethe data is currently in time series form we have the optionto continue using time series data or extract features fromit to feed into static classification algorithms.

4.2.1 Scalar FeaturesWe extracted several spectral features from each sensor foreach word for each person. Extraction was done in pythonusing the Pyeeg [15] package. The features used includedHjorth Parameters, Petrosian Fractal Dimension, Hurst Ex-ponent, and Spectral Entropy. These are defined as in [15].

4.2.1.1 Hjorth Parameters. Let X be a time series whereX = [x1, x2, ... , xN ]. Then the Hjorth Parameters are re-spectively,

Mobility =

√M2

TP(3)

Complexity =

√M4·TPM22

(4)

where TP =∑xi

N, M2 =

∑ diN

, M4 =∑ (di−di−1)

2

N, and

di = xi − xi−1.

4.2.1.2 Petrosian Fractal Dimension.

PFD =log10N

log10N + log10( NN+0.4Nδ

)(5)

where N is the series length, and Nδ is the number of signchanges in the signal derivative.

4.2.1.3 Hurst Exponent. Hurst exponent has also beencalled Rescaled Range statistics (R/S). Let X be a time se-ries where X = [x1, x2, ... , xN ]. Then let

X(t, T ) =

t∑i=1

(xi − x), x =1

T

T∑i=1

xi t∈[1, ..., N ] (6)

R(T)/S(T) is calculated as:

R(T )

S(T )=max(X(t, T )−min(X(t, T ))√

(1/T )T∑t=1

[x(t)− x]2

(7)

The Hurst exponent is the slope of the line produced byln(R(n)/S(n)) versus ln(n) for n∈[2, ..., N ].

4.2.1.4 Spectral Entropy. For this feature we used thebins normally associated with EEG/MEG which are, δ(0.5−4.0Hz), θ(4.0− 7.0Hz), α(8.0− 12.0Hz), β(12.0− 30.0Hz),and γ(30.0− 100.0Hz).

H = − 1

log(K)

K∑i=1

RIRilog(RIRi) (8)

where RIRi and K are defined in (2) of [15].

4.2.1.5 Fourier Transform. Fourier analysis converts asignal from its original domain to a representation in thefrequency domain [16]. The Fourier transform is defined as

F (k) =1√2π

∞∫−∞

e−ikxf(x)dx (9)

We use the FFT in the Python Numpy package for calcu-lation. More information can be had from Kutz [17] noteson computational methods for data analysis. We then chosethe top 3 power spectral density coefficients with their cor-responding frequencies. Figure 8 shows a plot of the powerspectral density (PSD) vs frequencies for a particular eegseries.

Figure 8: PSD for a sample EEG signal from theAF3 sensor

While it is certianly true that Fourier transforms can be usedin preprocessing to filter out unwanted frequencies, and thentransformed back into a time series domain using the inversetransform, we’ve opted to use the Fourier transformed dataitself to extract features from. Future work could certianlyentail changing this around.

4.2.2 Time Series FeaturesThis is an area where we didn’t get to explore as much aswe would have liked, however that said we would stil like to

report on the potential methods to consider

4.2.2.1 Motif Patterns. Suppes et al [12] and Oshri etal [5] used motif patterns in their analysis. These are theaverage signal produced amongst all subjects for a particularword and for a particular sensor. An example of what thislooks like is in figure 9.

Figure 9: Sample Motif Signal

4.2.2.2 Autoregressive models. An autoregressive modelis one that says future values can be modeled as a combina-tion of past values and noise. Equation 10 corresponds toan autoregressive (AR) model of order p.

Xt = c+

p∑i=1

ϕiXt−i + εi (10)

where ϕ1, ϕ2, ..., ϕp are the parameters of the model, c is aconstant and εi is white noise.

By fitting an AR(p) model on the motif signal, we can thentake that order model and fit it on all individual recordings.The estimated coefficients can become scalar features forclassification.

4.2.2.3 Preprocessed Data Itself. Since each EEG word,there are 14 channels with 254 samples each. By looking atthis as an image, we can have a 254 by 14 image and passthis into a classification algorithm that deals with images.Figure 10 shows the ”images” for the motif signal for twowords.

These are images that could potentially be passed into aconvolutional neural network (CNN).

4.3 Classification4.3.1 Linear Discriminant Analysis

Figure 10: Two words as images dog(left) andpink(right)

Linear discriminant analysis, or LDA for short, seeks tofind a linear combination of the features that separates ourclasses. While this is unlikely to be successful, it can still beuseful to guage the difficulty of the task at hand.

4.3.2 K Nearest NeighborsK Nearest Neighbors (KNN) allows inferences to be madeabout how distinct the words are from each other. It canbe a useful tool to see how successful our feature space is indrawing out salient linear features of the classes [5]. ShouldKNN have substantial accuracy then there should be realboundaries between the words. If on the other hand itsresults aren’t promising then very likely nonlinear pattersare needed to be considered.

4.3.3 Support Vector MachinesSiupport vector machines can be used for classification anal-ysis. The idea is to map the data into a higher dimen-sional space and find a hyperplane that that can seperatethe classes.

4.3.4 Decision TreesDecision trees are a non-parametric supervised learning methodused for classification and regression. Here the goal is to cre-ate a model that predicts the value of a target variable bylearning simple decision rules inferred form the data features[18].

4.3.5 Extra TreesThe extra-trees classifier fits a number of randomized deci-sion trees on various sub-samples of the dataset and thususes averaging to improve the predictive accuracy and con-trol over-fitting [18] which for our application is of criticalimportance.

4.3.6 Naive BayesNaive Bayes is one way to get around some of the prob-lems associated with KNN. In naive Bayes there are strong

independence assumptions between features, meaning theclassifier considers how each of the features contributed in-dependently to the probability that an instance belongs toa particular class.

4.3.7 AdaboostAdaboost is an ensemble classifier that begins by fitting aweak classifier on the original dataset (perhaps a stump,which is a very short decision tree). It will then repeat thisprocess on the same dataset but where the weights of incor-rectly classified instances are adjusted so that subsequentclassifiers have a higher chance of selecting those data forfitting [18].

4.3.8 Gradient Boosted TreesGradient Boosted Trees is a generalization of boosting toan arbitrary differentiable loss function, and thus it can beused for both regression and classification problems [18]. Wechose to include this classifier as it robust to outliers whichwould be a great property for modeling highly noisey datalike EEG data.

4.3.9 PerceptronThe perceptron is another linear classifier that instead ofusing probabilities to make its decision like the naive Bayesclassifier, will compute the class y whose weight vector ismost similar to the input vector.

4.3.10 Convolutional Neural NetworkThis is a widely used type of neural network that is scaleand shift invariant. We figured that if there is a signal thenit should be able to pick up something whether it be at thebeginning of the 2 second on screen time, or towards theend. However, we had a difficult time in fitting it gainingonly about 20

The features computed from the feature extraction sectionare the inputs into the above classifiers. The tables containthe cross-validated error along with ± 2 standard deviationsof the cross-validated error for all combinations of featuresinputted into the models. Because we are using 8 words, wehave 8 classes and seek to do better than 0.125 accuracy. Theclassification tables can be seen in figure 11 without usingthe wavelet transform. and in figure 12 with the wavelettransform using data from all 3 subjects.

5. CONCLUSION AND FUTURE WORKThe big takeaway from this research has been that analyz-ing EEG signals for the purpose of synthetic mind readingis challenging, but evidence shows it certainly looks possi-ble, especially given the right preprocessing, feature extrac-tion and classification algorithms. Based on the above cross-validation accuracies with their respective standard devia-tions it is clear that using wavelets for preprocessing gives asignificant boost in accuracy regardless of whether the tech-nique was linear or non-linear and the features used. Thissuggests that these features are quite different for differentclasses (words). As we are not experts in using wavelets thelevel and type of wavelet can certainly be tuned, but to dothis properly will likely require some more data. We arepleasantly surprised that we were able to get these kind of

Figure 11: Classification Accuracies without Wavelet Preprocessing

Figure 12: Classification Accuracies with Wavelet Preprocessing

results from a device that is magnitudes cheaper than de-vices used in prior work, and will continue working on thisproject in the future. There is still much work to be done!

As far as methods that we can currently consider, a largebody of work uses ICA when analyzing EEG signals and itwould likely be a great addition to the process flow. Unfor-tunately, we had a little difficulty incorporating it into thecurrent pipeline and so will need appropriate time to ensureit can be accomodated by our current code without messingthings up too much.

Since the Emotiv EPOC+ is a relatively cheap way to col-lect EEG data, and is fairly well known device we would liketo create a repository on github to release the experimentpublicly. Hopefully, this way we can crowd source data col-lection which, as far as we know, would be the first masscollection of EEG data for a particular task. By open sourc-ing the experiment we hope to make the data available to allusers who may be able to offer more targeted insights usingmore appropriate features and models. With a sufficiently

large quantity of decent quality data a whole other classof promising algorithms can be considered from the field ofdeep learning.

6. REFLECTIONIndividual contribution is rated with a score where the fullscore is 10.

Aaron Palmer: 10/10 Aaron Palmer is the starter of thisproject and made the most contribution to it. Based on themachine learning and statistics background, Aaron providesquite a bunch of ideas and solid theoretical support for thegroup. His contribution includes paper review, arrangingweekly group meeting (for the 1st half semester), work di-vision(for the 1st half semester), experiment design, datacollection, model selection, preprocessing implementation,classification implementation, report writing.

Yue Zhao: 10/10 As a graduate student with both computerscience and statistics background, Yue Zhao also made a

great contribution to the project. Similar to Aaron, YueZhao made significant contribution in paper review, arrang-ing weekly group meeting (for the 2nd half semester), work-load division (for the 2nd half semester), experiment design,volunteer collection, preprocessing implementation, classifi-cation implementation, report writing.

Yuwei Hu: 9/10 Yuwei is the third member of the team. Dueto lacking of machine learning background knowledge, Yuweiinstead made a greater contribution to attending weeklygroup discussion, experiment design, volunteer collection,preprocessing implementation, classification implementation,slides building, report writing.

Siming Li: 9/10 Siming did a good job on the communica-tion between the team and professor in BME departmentwhich provides significant progress for the whole project.Siming also made a great contribution to attending weeklygroup discussion, volunteer collection, classification imple-mentation, report writing.

Nari: 7/10 Due to the full time job Nari has, Nari did rel-atively less contribution to the project. Nari gets involvedin: attending weekly group discussion, volunteer collection,preprocessing implementation.

7. APPENDIX7.1 Additional Design Considerations

1. Instructions for the subjects: when a word comes intoa subject’s sight, multiple behaviour will be performedin one’s brain. e.g the spelling of the word, the pro-nounce of the word, the picture of the word if the wordhas some specific meaning, another language version ofthe word if the subject’s mother language is not En-glish (Actually it may be interesting to see how themodel will work if the word is in other languages whichare really different of English e.g Chinese Japanese),

memory recalled by this word if there anyaA ↪e... Thusideally the subjects should try to perform only one ofthe behaviours above and thus more significant patternwill be caught.

2. Troubling factors caused by individual difference: sincethe subjects are usually quite different, the effect tothe signal will tend to generate more noise too. Wehave to try our best to get rid of this effect throughdesign of experiment techniques in statistics area. Thefollowing factors often considered by the statisticianswill be discussed:

• language background: this could be one of themost significant factors that could affect the sig-nal outcome. For example when seeing the word’dog’, many Chinese student will naturally thinkof the Chinese word which cause the signal to besignificantly different from native speakers. Thuslanguage background could be regarded as a blockfactor in the design. The factor has two levels,native speaker and nonnative speaker.

• age: this factor may also affect the signal butsince our subjects are all young 20s, and the in-creasing model complexity is pretty expensive thuswe decide to neglect this factor for now.

• gender: gender may not be an important factorwhen it comes to language processing in braineven though girls and boys may have extremelydifferent decision mechanism.

• races: this is also an important factor but wethink the effect of this factor may reduce to thelanguage background factor. Comparing Ameri-can Born Asian and Native Asian, we are likelyto find that the signals are really different for theformer may have more similar signal to the otherAmericans

• time of the day: The human body operates witha period of the 24 hours and the brain would bein different status too. This will also significantlyaffect the signal we collect. Imagine the momentswhen you just have a cup of coffee around 9 a.m.and the moments when after just finish your lunch,we could easily the difference of your brain status.Adding this factor to the experiment could be ex-pensive considering of model complexity (whichwill lead to a latin square design) and that it isreally hard to define specific brain status in 24hours.

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