Objective: Brain-computer interfaces (BCIs) aimed at restoring communication to people with severe neuromuscular disabilities often use event-related potentials (ERPs) in scalp-recorded EEG activity. Up to the present, most research and development in this area has been done in the laboratory with young healthy control subjects. In order to facilitate the development of BCI most useful to people with disabilities, the present study set out to: (1) determine whether people with amyotrophic lateral sclerosis (ALS) and healthy, age-matched volunteers (HVs) differ in the speed and accuracy of their ERP-based BCI use; (2) compare the ERP characteristics of these two groups; and (3) identify ERP-related factors that might enable improvement in BCI performance for people with disabilities. Methods: Sixteen EEG channels were recorded while people with ALS or healthy age-matched volunteers (HVs) used a P300-based BCI. The subjects with ALS had little or no remaining useful motor control (mean ALS Functional Rating Scale-Revised 9.4 (±9.5SD) (range 0-25)). Each subject attended to a target item as the items in a 6. ×. 6 visual matrix flashed. The BCI used a stepwise linear discriminant function (SWLDA) to determine the item the user wished to select (i.e., the target item). Offline analyses assessed the latencies, amplitudes, and locations of ERPs to the target and non-target items for people with ALS and age-matched control subjects. Results: BCI accuracy and communication rate did not differ significantly between ALS users and HVs. Although ERP morphology was similar for the two groups, their target ERPs differed significantly in the location and amplitude of the late positivity (P300), the amplitude of the early negativity (N200), and the latency of the late negativity (LN). Conclusions: The differences in target ERP components between people with ALS and age-matched HVs are consistent with the growing recognition that ALS may affect cortical function. The development of BCIs for use by this population may begin with studies in HVs but also needs to include studies in people with ALS. Their differences in ERP components may affect the selection of electrode montages, and might also affect the selection of presentation parameters (e.g., matrix design, stimulation rate). Significance: P300-based BCI performance in people severely disabled by ALS is similar to that of age-matched control subjects. At the same time, their ERP components differ to some degree from those of controls. Attention to these differences could contribute to the development of BCIs useful to those with ALS and possibly to others with severe neuromuscular disabilities.

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Interfaces cérebro-computador ou Interfaces cérebro-máquina (BCIs/BMIs do inglês Brain-computer interface/Brain-machine interface) são dispositivos que permitem ao usuário interagir com o ambiente ao seu redor sem que seja necessário ativar seus músculos esqueléticos. Estes dispositivos são de extrema valia para indivíduos portadores de deficiências motoras. Esta dissertação ambiciona revisar a literatura acerca de BMIs e expor diferentes técnicas de pré-processamento, extração de características e classificação de sinais neurofisiológicos. Em particular, uma maior ênfase será dada à Máquina de vetor de suporte (SVM do inglês Support-Vector machine), método de classificação baseado no princípio da minimização do risco estrutural. Será apresentado um estudo de caso, que ilustra o funcionamento de uma BMI, a qual permite ao usuário escolher um dentre seis objetos mostrados em uma tela de computador. Esta capacidade da BMI é conseqüência da implementação, através da SVM de um sistema capaz de detectar o potencial evocado P300 nos sinais de eletroencefalograma (EEG). A simulação será realizada em Matlab usando, como sinais de entrada, amostras de EEG de quatro indivíduos saudáveis e quatro deficientes. A análise estatística mostrou que o bom desempenho obtido pela BMI (80,73% de acerto em média) foi promovido pela aplicação da média coerente aos sinais, o que melhorou a relação sinal-ruído do EEG. / Brain-computer interfaces (BCIs) or Brain-machine interfaces (BMIs) technology provide users with the ability to communicate and control their environment without employing normal output pathway of peripheral nerves and muscles. This technology can be especially valuable for highly paralyzed patients. This thesis reviews BMI research, techniques for preprocessing, feature extracting and classifying neurophysiological signals. In particular, emphasis will be given to Support-Vector Machine (SVM), a classification technique, which is based on structural risk minimization. Additionally, a case study will illustrate the working principles of a BMI which analyzes electroencephalographic signals in the time domain as means to decide which one of the six images shown on a computer screen the user chose. The images were selected according to a scenario where users can control six electrical appliances via a BMI system. This was done by exploiting the Support-Vector Machine ability to recognize a specific EEG pattern (the so-called P300). The study was conducted offline within the Matlab environment and used EEG datasets recorded from four disabled and four able-bodied subjects. A statistical survey of the results has shown that the good performance attained (80,73%) was due to signal averaging method, which enhanced EEG signal-to-noise ratio.

Interfaces cérebro-computador ou Interfaces cérebro-máquina (BCIs/BMIs do inglês Brain-computer interface/Brain-machine interface) são dispositivos que permitem ao usuário interagir com o ambiente ao seu redor sem que seja necessário ativar seus músculos esqueléticos. Estes dispositivos são de extrema valia para indivíduos portadores de deficiências motoras. Esta dissertação ambiciona revisar a literatura acerca de BMIs e expor diferentes técnicas de pré-processamento, extração de características e classificação de sinais neurofisiológicos. Em particular, uma maior ênfase será dada à Máquina de vetor de suporte (SVM do inglês Support-Vector machine), método de classificação baseado no princípio da minimização do risco estrutural. Será apresentado um estudo de caso, que ilustra o funcionamento de uma BMI, a qual permite ao usuário escolher um dentre seis objetos mostrados em uma tela de computador. Esta capacidade da BMI é conseqüência da implementação, através da SVM de um sistema capaz de detectar o potencial evocado P300 nos sinais de eletroencefalograma (EEG). A simulação será realizada em Matlab usando, como sinais de entrada, amostras de EEG de quatro indivíduos saudáveis e quatro deficientes. A análise estatística mostrou que o bom desempenho obtido pela BMI (80,73% de acerto em média) foi promovido pela aplicação da média coerente aos sinais, o que melhorou a relação sinal-ruído do EEG. / Brain-computer interfaces (BCIs) or Brain-machine interfaces (BMIs) technology provide users with the ability to communicate and control their environment without employing normal output pathway of peripheral nerves and muscles. This technology can be especially valuable for highly paralyzed patients. This thesis reviews BMI research, techniques for preprocessing, feature extracting and classifying neurophysiological signals. In particular, emphasis will be given to Support-Vector Machine (SVM), a classification technique, which is based on structural risk minimization. Additionally, a case study will illustrate the working principles of a BMI which analyzes electroencephalographic signals in the time domain as means to decide which one of the six images shown on a computer screen the user chose. The images were selected according to a scenario where users can control six electrical appliances via a BMI system. This was done by exploiting the Support-Vector Machine ability to recognize a specific EEG pattern (the so-called P300). The study was conducted offline within the Matlab environment and used EEG datasets recorded from four disabled and four able-bodied subjects. A statistical survey of the results has shown that the good performance attained (80,73%) was due to signal averaging method, which enhanced EEG signal-to-noise ratio.

In this work, the problem of detecting neuronal spikes or action potentials (AP) in noisy recordings from a Microelectrode Array (MEA) is investigated. In particular, the spike detection algorithms should be less complex and with low computational complexity so as to be amenable for real time applications. The use of the MEA is that it allows collection of extracellular signals from either a single unit or multiple (45) units within a small area. The noisy MEA recordings then undergo basic filtering, digitization and are presented to a computer for further processing. The challenge lies in using this data for detection of spikes from neuronal firings and extracting spatiotemporal patterns from the spike train which may allow control of a robotic limb or other neuroprosthetic device directly from the brain. The aim is to understand the spiking action of the neurons, and use this knowledge to devise efficient algorithms for Brain Machine Interfaces (BMIs). An effective BMI will require a realtime, computationally efficient implementation which can be carried out on a DSP board or FPGA system. The aim is to devise algorithms which can detect spikes and underlying spatio-temporal correlations having computational and time complexities to make a real time implementation feasible on a specialized DSP chip or an FPGA device. The time-frequency localization, multiresolution representation and analysis properties of wavelets make them suitable for analysing sharp transients and spikes in signals and distinguish them from noise resembling a transient or the spike. Three algorithms for the detection of spikes in low SNR MEA neuronal recordings are proposed: 1. A wavelet denoising method based on the Discrete Wavelet Transform (DWT) to suppress the noise power in the MEA signal or improve the SNR followed by standard thresholding techniques to detect the spikes from the denoised signal. 2. Directly thresholding the coefficients of the Stationary (Undecimated) Wavelet Transform (SWT) to detect the spikes. 3. Thresholding the output of a Teager Energy Operator (TEO) applied to the signal on the discrete wavelet decomposed signal resulting in a multiresolution TEO framework. The performance of the proposed three wavelet based algorithms in terms of the accuracy of spike detection, percentage of false positives and the computational complexity for different types of wavelet families in the presence of colored AR(5) (autoregressive model with order 5) and additive white Gaussian noise (AWGN) is evaluated. The performance is further evaluated for the wavelet family chosen under different levels of SNR in the presence of the colored AR(5) and AWGN noise. Chapter 1 gives an introduction to the concept behind Brain Machine Interfaces (BMIs), an overview of their history, the current state-of-the-art and the trends for the future. It also describes the working of the Microelectrode Arrays (MEAs). The generation of a spike in a neuron, the proposed mechanism behind it and its modeling as an electrical circuit based on the Hodgkin-Huxley model is described. An overview of some of the algorithms that have been suggested for spike detection purposes whether in MEA recordings or Electroencephalographic (EEG) signals is given. Chapter 2 describes in brief the underlying ideas that lead us to the Wavelet Transform paradigm. An introduction to the Fourier Transform, the Short Time Fourier Transform (STFT) and the Time-Frequency Uncertainty Principle is provided. This is followed by a brief description of the Continuous Wavelet Transform and the Multiresolution Analysis (MRA) property of wavelets. The Discrete Wavelet Transform (DWT) and its filter bank implementation are described next. It is proposed to apply the wavelet denoising algorithm pioneered by Donoho, to first denoise the MEA recordings followed by standard thresholding technique for spike detection. Chapter 3 deals with the use of the Stationary or Undecimated Wavelet Transform (SWT) for spike detection. It brings out the differences between the DWT and the SWT. A brief discussion of the analysis of non-stationary time series using the SWT is presented. An algorithm for spike detection based on directly thresholding the SWT coefficients without any need for reconstructing the denoised signal followed by thresholding technique as in the first method is presented. In chapter 4 a spike detection method based on multiresolution Teager Energy Operator is discussed. The Teager Energy Operator (TEO) picks up localized spikes in signal energy and thus is directly used for spike detection in many applications including R wave detection in ECG and various (alpha, beta) rhythms in EEG. Some basic properties of the TEO are discussed followed by the need for a multiresolution approach to TEO and the methods existing in literature. The wavelet decomposition and the subsampled signal involved at each level naturally lends it to a multiresolution TEO framework at the same time significantly reducing the computational complexity due the subsampled signal at each level. A wavelet-TEO algorithm for spike detection with similar accuracies as the previous two algorithms is proposed. The method proposed here differs significantly from that in literature since wavelets are used instead of time domain processing. Chapter 5 describes the method of evaluation of the three algorithms proposed in the previous chapters. The spike templates are obtained from MEA recordings, resampled and normalized for use in spike trains simulated as Poisson processes. The noise is modeled as colored autoregressive (AR) of order 5, i.e AR(5), as well as Additive White Gaussian Noise (AWGN). The noise in most human and animal MEA recordings conforms to the autoregressive model with orders of around 5. The AWGN Noise model is used in most spike detection methods in the literature. The performance of the proposed three wavelet based algorithms is measured in terms of the accuracy of spike detection, percentage of false positives and the computational complexity for different types of wavelet families. The optimal wavelet for this purpose is then chosen from the wavelet family which gives the best results. Also, optimal levels of decomposition and threshold factors are chosen while maintaining a balance between accuracy and false positives. The algorithms are then tested for performance under different levels of SNR with the noise modeled as AR(5) or AWGN. The proposed wavelet based algorithms exhibit a detection accuracy of approximately 90% at a low SNR of 2.35 dB with the false positives below 5%. This constitutes a significant improvement over the results in existing literature which claim an accuracy of 80% with false positives of nearly 10%. As the SNR increases, the detection accuracy increases to close to 100% and the false alarm rate falls to 0. Chapter 6 summarizes the work. A comparison is made between the three proposed algorithms in terms of detection accuracy and false positives. Directions in which future work may be carried out are suggested.

Brain-computer interface (BCI) or Brain Machine Interface (BMI) provides direct communication channel between user’s brain and an external device without any requirement of user’s physical movement. Primarily BCI has been employed in medical sciences to facilitate the patients with severe motor, visual and aural impairments. More recently many BCI are also being used as a part of entertainment. BCI differs from Neuroprosthetics, a study within Neuroscience, in terms of its usage; former connects the brain with a computer or external device while the later connects the nervous system to an implanted device. A BCI receives the modulated input from user either invasively or non-invasively. The modulated input, concealed in the huge amount of noise, contains distinct brain patterns based on the type of activity user is performing at that point in time. Primary task of a typical BCI is to find out those distinct brain patterns and translates them to meaningful communication command set. Cursor controllers, Spellers, Wheel Chair and robot Controllers are classic examples of BCI applications. This study aims to investigate an Electroencephalography (EEG) based non-invasive BCI in general and its interaction with a web interface in particular. Different aspects related to BCI are covered in this work including feedback techniques, BCI frameworks, commercial BCI hardware, and different BCI applications. BCI paradigm Steady State Visually Evoked Potentials (SSVEP) is being focused during this study. A hybrid solution is developed during this study, employing a general purpose BCI framework OpenViBE, which comprised of a low-level stimulus management and control module and a web based Google Street View client application. This study shows that a BCI can not only provide a way of communication for the impaired subjects but it can also be a multipurpose tool for a healthy person. During this study, it is being established that the major hurdles that hamper the performance of a BCI system are training protocols, BCI hardware and signal processing techniques. It is also observed that a controlled environment and expert assistance is required to operate a BCI system.

There are several commercial electroencephalography (EEG) devices on the market; however, affordable devices are not versatile for diverse research applications. The purpose of this project was to investigate how to develop a low-cost, portable, single-channel EEG system for a research institute that could be used for neurofeedback-related applications in homecare. A device comparison was intended to examine what system requirements such a system would need to achieve the secondary objective of developing a neurofeedback application that demonstrates the functionalities of the new device. A portable, single-channel EEG device prototype was realized that consisted of an amplifier module called EEG Click, a single-board microcontroller, an electrode cable, some disposable wet electrode pads, and a custom 3D-printed headband. Three pieces of software were developed: firmware for the prototype, two supporting computer applications for data recording, and visual neurofeedback. The neurofeedback application replayed a first-person view roller coaster video at a varying frame rate based on the theta band's mean power spectral density (PSD). The prototype was compared against a commercial device, InteraXon MUSE 2 (Muse). Technical measurements included determining the amplitude-frequency characteristics and signal quality, such as signal-to-noise ratio (SNR), spurious-free dynamic range (SFDR), and total harmonic distortion (THD). Furthermore, four physiological measurements were performed on six human test subjects, aged between 21-31 (mean: 26.0, std: 3.11), to compare the altered brain activity and induced artifacts between the two devices. The four tests were respiratory exercise, head movement exercise, eye movement exercise, and paced auditory serial addition test (PASAT), where each measurement included several epochs with various stimuli. After the recordings, PSD was calculated for each bandpass filtered epoch, then the spectra were split into theta (4-8 Hz), alpha (8-12 Hz), and beta bands (12-30 Hz). The PSD values were averaged within each frequency band, and then these baseline-corrected mean values were the input for the repeated measures ANOVA statistical analysis. Results revealed that the amplitude-frequency characteristic of the prototype was low-pass filter-like and had a smaller slope than Muse's. The prototype's SNR, including and excluding the first five harmonics, was 6 dB higher, while SFDR and THD for the first five harmonics were roughly the same as Muse's. The two devices were comparable in detecting changes in most physiological measurements. Some differences between the two devices were that Muse was able to detect changes in respiratory activity in the beta band (F(8,16) = 2.510, p = .056), while the prototype was more sensitive to eye movement, especially lateral and circular eye movement in theta (F(2,8) = 9.144, p = .009) and alpha (F(2,8) = 6.095, p = .025) bands. A low-cost, portable EEG prototype was successfully realized and validated. The prototype was capable of performing homecare neurofeedback in the theta band. The results indicated it is worth exploring further the capabilities of the prototype. Since the sample size was too small, more complex physiological measurements with more test subjects would be more conclusive. Nevertheless, the findings are promising; the prototype may become a product once. / Det finns flera kommersiella EEG-apparater (elektroencefalografi) på marknaden; däremot är de prismässigt överkomliga apparaterna inte mångsidiga nog för olika forskningsapplikationer. Syftet med detta projekt var att undersöka hur man kan utveckla en billigt, portabelt, enkanaligt EEG-system för ett forskningsinstitut som skulle kunna användas för neurofeedbackrelaterade tillämpningar inom hemsjukvård. En apparatjämförelse var tänkt att undersöka vilka systemkrav ett sådant system skulle behöva uppnå för att realisera det sekundära målet att utveckla en neurofeedback-applikation för att demonstrera den nya apparatens funktioner. En prototyp av en bärbar, enkanalig EEG-apparat skapades som bestod av en förstärkarmodul kallad EEG Click, en enkortsmikrokontroller, en elektrodkabel, några utbytbara våta elektrodkuddar och ett 3D-tryckt specialpannband. Tre mjukvarodelar utvecklades: en firmware för prototypen och två stödjande datorapplikationer, en för datainspelning och en för visuell neurofeedback. Applikationen för neurofeedback spelade upp en berg-och-dalbana för förstapersonsvisning med en varierande bildhastighet baserat på thetabandets effektspektrumet (eng. power spectral density, PSD). Prototypen jämfördes mot en kommersiell apparat, InteraXon MUSE 2 (Muse). Tekniska mätningar inkluderade fastställande av amplitud-frekvensegenskaper och signalkvalitet, såsom signal-brusförhållande (eng. signal-to-noise ratio, SNR), spuriosfritt dynamiskt område (eng. spurious free dynamic range, SFDR) och total harmonisk distorsion (eng. total harmonic distortion, THD). Vidare utfördes fyra fysiologiska mätningar på sex mänskliga deltagare (medelålder: 26,0, std: 3,11) för att jämföra de två apparaterna med avseende på mätningar av den förändrade hjärnaktiviteten och inducerade artefakter. De fyra testerna var andningsövningar, huvudrörelseövningar, ögonrörelseövningar, och paced auditory serial addition test (PASAT), där varje mätning innehöll flera epoker med olika stimuli. Efter inspelningarna beräknades PSD för varje bandpassfiltrerad epok, sedan delades spektrumet upp i theta-, alpha- och beta-band. Medelvärdet för PSD-värdena kalkylerades för varje frekvensband och dessa baseline-korrigerade medelvärden var indata till den beroende ANOVA statistisk analysen. Resultaten avslöjade att amplitud-frekvenskarakteristiken för prototypen var lågpassfilterliknande och hade en mindre lutning än Muses. Prototypens SNR, inklusive och exklusive de första fem harmonik, var 6 dB högre, medan SFDR och THD för de första fem övertonerna var ungefär desamma som Muses. De två apparaterna var jämförbara när det gäller att upptäcka förändringar i de flesta fysiologiska mätningar. Vissa skillnader mellan de två apparaterna var att Muse kunde upptäcka förändringar i andningsaktivitet i beta-bandet (F(8,16) = 2,510, p = 0,056), medan prototypen var mer känslig för ögonrörelser, särskilt de laterala och cirkulära ögonrörelser, i theta-bandet (F(2,8) = 9,144, p = 0,009) och alfa-bandet (F(2,8) = 6,095, p = 0,025). Prototypen var generellt mer känslig för grundläggande hjärnaktivitet, buller från omgivningen och artefakter. Sammanfattningvis konstruerades en billig, bärbar EEG-prototyp, vilketvaliderades framgångsrikt. Den anpassade enheten kunde utföra neurofeedback för hemsjukvård. Resultaten visade att det är värt att utforska prototypens möjligheter ytterligare. Eftersom stickprovet var relativt litet skulle mer komplexa fysiologiska mätningar med flera testpersoner krävas för att fastställa framtida användningsområden. Icke desto mindre är resultaten lovande; prototypen kan bli en produkt en gång.

Chronic recording of neural signals is indispensable in designing efficient brain machine interfaces and to elucidate human neurophysiology. The advent of multi-channel micro-electrode arrays has driven the need for electronic store cord neural signals from many neurons. The continuous increase in demand of data from more number of neurons is challenging for the design of an efficient neural recording frontend(NRFE). Power consumption per channel and data rate minimization are two key problems which need to be addressed by next generation of neural recording systems. Area consumption per channel must be low for small implant size. Dynamic range in NRFE can vary with time due to change in electrode-neuron distance or background noise which demands adaptability. In this thesis, techniques to reduce power-per-channel and area-per-channel in a NRFE, via new circuits and architectures, are proposed. An area efficient low power neural LNA is presented in UMC 0.13 μm 1P8M CMOS technology. The amplifier can be biased adaptively from 200 nA to 2 μA , modulating input referred noise from 9.92 μV to 3.9μV . We also describe a low noise design technique which minimizes the noise contribution of the load circuitry. Optimum sizing of the input transistors minimizes the accentuation of the input referred noise of the amplifier. It obviates the need of large input coupling capacitance in the amplifier which saves considerable amount of chip area. In vitro experiments were performed to validate the applicability of the neural LNA in neural recording systems. ADC is another important block in a NRFE. An 8-bit SAR ADC along with the input and reference buffer is implemented in 0.13 μm CMOS technology. The use of ping-pong input sampling is emphasized for multichannel input to alleviate the bandwidth requirement of the input buffer. To reduce the output data rate, the A/D process is only enabled through a proposed activity dependent A/D scheme which ensures that the background noise is not processed. Based on the dynamic range requirement, the ADC resolution is adjusted from 8 to 1 bit at 1 bit step to reduce power consumption linearly. The ADC consumes 8.8 μW from1Vsupply at1MS/s and achieves ENOB of 7.7 bit. The ADC achieves FoM of 42.3 fJ/conversion in 0.13 μm CMOS technology. Power consumption in SARADCs is greatly benefited by CMOS scaling due to its highly digital nature. However the power consumption in the capacitive DAC does not scale as well as the digital logic. In this thesis, two energy-efficient DAC switching techniques, Flip DAC and Quaternary capacitor switching, are proposed to reduce their energy consumption. Using these techniques, the energy consumption in the DAC can be reduced by 37 % and 42.5 % compared to the present state-of-the-art. A novel concept of code-independent energy consumption is introduced and emphasized. It mitigates energy consumption degradation with small input signal dynamic range.