Dissertations / Theses on the topic 'Steady-State Visually Evoked Potential (SSVEP)'

In the last two decades, Brain-Computer Interfaces (BCIs) have been investigated mainly for the purpose of implementing assistive technologies able to provide new channels for communication and control for people with severe disabilities. Nevertheless, more recently, thanks to technical and scientific advances in the different research fields involved, BCIs are gaining greater attention also for their adoption by healthy users, as new interaction devices. This thesis is dedicated to to the latter goal and in particular will deal with BCIs based on the Steady State Visual Evoked Potential (SSVEP), which in previous works demonstrated to be one of the most flexible and reliable approaches. SSVEP based BCIs could find applications in different contexts, but one which is particularly interesting for healthy users, is their adoption as new interaction devices for Virtual Reality (VR) environments and Computer Games. Although being investigated since several years, BCIs still poses several limitations in terms of speed, reliability and usability with respect to ordinary interaction devices. Despite of this, they may provide additional, more direct and intuitive, explicit interaction modalities, as well as implicit interaction modalities otherwise impossible with ordinary devices. This thesis, after a comprehensive review of the different research fields being the basis of a BCI exploiting the SSVEP modality, present a state-of-the-art open source implementation using a mix of pre-existing and custom software tools. The proposed implementation, mainly aimed to the interaction with VR environments and Computer Games, has then been used to perform several experiments which are hereby described as well. Initially performed experiments aim to stress the validity of the provided implementation, as well as to show its usability with a commodity bio-signal acquisition device, orders of magnitude less expensive than commonly used ones, representing a step forward in the direction of practical BCIs for end users applications. The proposed implementation, thanks to its flexibility, is used also to perform novel experiments aimed to investigate the exploitation of stereoscopic displays to overcome a known limitation of ordinary displays in the context of SSVEP based BCIs. Eventually, novel experiments are presented investigating the use of the SSVEP modality to provide also implicit interaction. In this context, a first proof of concept Passive BCI based on the SSVEP response is presented and demonstrated to provide information exploitable for prospective applications.

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.

This masters thesis examines the hemispheric activation pattern of the cognitive processes involved in a complex mental rotations test (MRT) (Vandenberg and Kuse, 1978) using Steady-State Probe Topography (SSPT) (Silberstein et al, 1990) as a method to index brain activity. The Steady State Visually Evoked Potential (SSVEP) was recorded from 64 electrode sites using a multichannel electrode helmet, and elicited by a 13 Hz sinusoidal visual flicker, whilst the subjects were performing a visual vigilance Baseline task and the MRT. Forty-one right handed subjects (twenty male and twenty-one female) were used. In the MRT the subjects were required to choose the two figures which correctly matched the criterion figure in the centre. The figures were three-dimensional objects represented in two-dimensions on a computer screen. A significant finding of this study was that when all the subjects were considered as one group, no noticeable lateralization in cerebral activation associated with mental rotation was evident. When analyzing the results for the subjects, partitioned into two groups according to gender, evidence was found suggesting that the cortical processing associated with mental rotation may be more localized bilaterally in the males than the females. However, no noticeable lateralization effects for mental rotation were found in the males or females, and hence no gender differences in hemispheric lateralization was evident. An important finding was the emergence of gender differences in hemispheric lateralization in subsets of subjects performing with higher spatial ability. A left hemisphere lateralization for mental rotation was associated with the Best Performance Male group. The Best Performance Female group showed the opposite effect, where a right hemisphere lateralization was associated with better performance on the task. The lateralization effect appeared to be stronger in the Best Performance Males than the Best Performance Females. An important conclusion from this study is that when examining for hemispheric lateralization effects in mental rotation, and possibly other visual-spatial tasks, not only gender effects need to be considered, but the level of spatial ability in the comparison groups needs also to be taken into account.

Object recognition memory (ORM) refers to both recognition of an object and the memory of having seen it before. In humans, ORM has been investigated using functional neuroimaging and electrophysiological techniques with tests of episodic memory retrieval involving recollection of previously studied items. Processes involved in the maintenance of a mental state adopted for the performance of a retrieval task (retrieval mode) appear to involve right frontal neural regions. More transient processes occurring at the time of item recollection (retrieval success) have shown scalp activity over parietal and right frontal regions. This activity is thought to originate in the medial temporal lobes and the underlying right frontal cortex respectively. The aforementioned findings have been derived mainly from studies using verbal stimuli. It is uncertain whether the same neural regions are involved in object recollection. It is also uncertain whether sustained modal and transient item-related activity involve the same or different right frontal regions. In this study, steady-state probe topography (SSPT) was used to investigate both sustained and transient processes involved in the retrieval of abstract pictorial objects from memory. The ability to vary the evaluation period of the steady-state visually evoked potential (SSVEP) allows investigation of cognitive processes occurring over different time scales. Neural regions involved in sustained modal processes were identified by examining the SSVEP values averaged over the duration of a memory retrieval task. Sustained SSVEP effects were observed over right fronto-temporal regions. Neural regions involved in transient retrieval success processes were identified by comparing the transient SSVEP responses to tasks with different memory loads. Comparison of a higher with a lower memory load condition showed SSVEP effects over parieto-temporal and right inferior frontal regions. Larger differences between memory loads gave effects that were larger and more right lateralized. Retrieval mode and retrieval success processes showed SSVEP effects over different right frontal regions. It was also found that, in contrast to the left lateralized parietal ERP response to recollected verbal stimuli, the SSVEP effects produced with abstract pictorial shapes showed a more bilateral pattern. This was considered to reflect the relatively non-verbalizable pictorial nature of the stimuli.

Thesis (M.Sc.) - Swinburne University of Technology, Brain Sciences Institute, 2008.
A thesis submitted for M.Sc by Research, Brain Sciences Institute, Swinburne University of Technology - 2008. Typescript. Includes bibliographical references (p. 117-153)

Una Brain Computer Interface (BCI) è un dispositivo che permette la misura e l’utilizzo di segnali cerebrali al fine di comandare software e/o periferiche di vario tipo, da semplici videogiochi a complesse protesi robotizzate. Tra i segnali attualmente più utilizzati vi sono i Potenziali Evocati Visivi Steady State (SSVEP), variazioni ritmiche di potenziale elettrico registrabili sulla corteccia visiva primaria con un elettroencefalogramma (EEG) non invasivo; essi sono evocabili attraverso una stimolazione luminosa periodica, e sono caratterizzati da una frequenza di oscillazione pari a quella di stimolazione. Avendo un rapporto segnale rumore (SNR) particolarmente favorevole ed una caratteristica facilmente studiabile, gli SSVEP sono alla base delle più veloci ed immediate BCI attualmente disponibili. All’utente vengono proposte una serie di scelte ciascuna associata ad una stimolazione visiva a diversa frequenza, fra le quali la selezionata si ripresenterà nelle caratteristiche del suo tracciato EEG estratto in tempo reale. L’obiettivo della tesi svolta è stato realizzare un sistema integrato, sviluppato in LabView che implementasse il paradigma BCI SSVEP-based appena descritto, consentendo di: 1. Configurare la generazione di due stimoli luminosi attraverso l’utilizzo di LED esterni; 2. Sincronizzare l’acquisizione del segnale EEG con tale stimolazione; 3. Estrarre features (attributi caratteristici di ciascuna classe) dal suddetto segnale ed utilizzarle per addestrare un classificatore SVM; 4. Utilizzare il classificatore per realizzare un’interfaccia BCI realtime con feedback per l’utente. Il sistema è stato progettato con alcune delle tecniche più avanzate per l’elaborazione spaziale e temporale del segnale ed il suo funzionamento è stato testato su 4 soggetti sani e comparato alle più moderne BCI SSVEP-based confrontabili rinvenute in letteratura.

Les Interfaces Cerveau Ordinateur (ICO) permettent l’interaction à partir de l’activité cérébrale. La Réalité Augmentée (RA) elle, permet d’intégrer des éléments virtuels dans un environnement réel. Dans cette thèse, nous avons cherché à concevoir des systèmes interactifs exploitant des ICO dans des environnements RA, afin de proposer de nouveaux moyens d’interagir avec des éléments réels et virtuels. Dans la première partie de cette thèse, nous avons étudié la possibilité d’extraire différents signaux cérébraux dans un contexte de RA. Nous avons ainsi montré qu’il était possible d’exploiter les Potentiels Evoqués Visuels Stationnaires (SSVEP) en RA. Puis, nous avons montré la possibilité d’extraire des Potentiels d’Erreur des signaux cérébraux, lorsqu’un utilisateur est soumis à des types d’erreurs fréquents en RA. Dans la seconde partie, nous avons approfondi nos recherches sur l’utilisation des SSVEP pour l’interaction en RA. Nous avons notamment proposé HCCA, un nouvel algorithme permettant la reconnaissance asynchrone de réponses SSVEP. Nous avons ensuite étudié la conception d’interfaces de RA, pour des systèmes interactifs, intuitifs performants. Enfin nous avons illustré nos résultats à travers le développement d’un système de domotique utilisant les SSVEP et la RA, qui s’intègre à une plateforme de maison intelligente industrielle
Brain-Computer Interfaces (BCI) enable interaction directly from brain activity. Augmented Reality (AR) on the other hand, enables the integration of virtual elements in the real world. In this thesis, we aimed at designing interactive systems associating BCIs and AR, to offer new means of hands-free interaction with real and virtual elements. In the first part, we have studied the possibility to extract different BCI paradigms in AR. We have shown that it was possible to use Steady-State Visual Evoked Potentials (SSVEP) in AR. Then, we have studied the possibility to extract Error-Related Potentials (ErrPs) in AR, showing that ErrPs were elicited in users facing errors, often occurring in AR. In the second part, we have deepened our research in the use of SSVEP for direct interaction in AR. We have proposed HCCA, a new algorithm for self-paced detection of SSVEP responses. Then, we have studied the design of AR interfaces, for the development of intuitive and efficient interactive systems. Lastly, we have illustrated our results, through the development of a smart-home system combining SSVEP and AR, which integrates in a commercially available smart-home system

Con il termine Brain-Computer Interface (BCI) si indica un sistema hardware e software che permette, a partire da segnali di origine cerebrale, di tradurre le intenzioni dell’utente in comandi per il controllo di un dispositivo di output, come computer, sintetizzatori vocali, apparecchi di assistenza e neuroprotesi. Questa tipologia di applicazioni non richiede l’impiego di muscoli periferici, poiché sfrutta solamente specifici segnali generati dall’attività cerebrale. In particolare, il presente elaborato tratta le BCI-speller, ovvero sistemi che permettono la scrittura di un testo sfruttando le variazioni del segnale elettroencefalografico (EEG) suscitate attraverso un’interfaccia grafica (GUI). La GUI è costituita da lettere, simboli e numeri opportunamente presentati, così che, se il soggetto presta attenzione ad uno di essi, particolari potenziali cerebrali vengono elicitati nell’EEG e sfruttati per identificare e quindi selezionare tale simbolo. L’obiettivo dell’elaborato è introdurre l’emergente e promettente campo di ricerca delle BCI, facendo luce sulle caratteristiche dei componenti che le caratterizzano e sulle varie applicazioni a cui si prestano, concentrandosi sulle BCI-speller. A tal fine, sono state presentate BCI-speller basate su due particolari potenziali cerebrali (P300 e SSVEP), ponendo particolare attenzione sugli aspetti che possono portare ad un loro miglioramento. Maggiore enfasi è stata quindi posta sulle diverse GUI delle BCI-speller basate su questi potenziali, in quanto modifiche associate alla presentazione dello stimolo e alla facilità d’uso di queste interfacce possono migliorare in prima battuta il potenziale elicitato, e di conseguenza la prestazione generale della BCI. Infine, sono stati evidenziati i vantaggi e i limiti associati a queste tecnologie, nonché gli sviluppi futuri nella prospettiva di un impiego quotidiano di queste tecnologie da parte di pazienti affetti da disturbi neuromuscolari.

In the last decade recurrent neural networks have revolutionized the field of artificial intelligence. Their cyclic connections provide them with memory and thus with the capability of modeling processes with temporal context. Echo-state networks are a framework for recurrent neural networks that enormously simplifies their design and training. In this thesis we explore the capabilities of echo-state networks and their application in EEG feature extraction and classification problems. In a first study, we proved that such networks are capable of detecting generalized synchronization changes between two chaotic time-series. In a second study, we used echo-state networks to characterize the non-stationary nature of what has been considered so far to be a stationary brain response, namely steady-state visual evoked potentials (SSVEPs). Finally, in a third study, we successfully proposed a novel biomarker for attention deficit hyperactivity disorder (ADHD), which is capable of quantifying EEG dynamical changes between low and normal attention-arousal conditions. The results presented here demonstrate the excellent non-stationary detection capabilities of these networks, and their applicability to electrophysiological data analysis.
En la última decada las redes neuronales recurrentes han revolucionado el campo de la inteligencia artificial. Sus conexiones cíclicas les proporcionan memoria y por tanto la capacidad de modelar problemas con contexto temporal. Las redes echo-state simplifican enormemente el diseño y entrenamiento de las redes recurrentes. En esta tesis exploramos el uso de redes echo-state y su aplicación en problemas de clasificación y detección de patrones en señales EEG. En un primer estudio demostramos que son capaces de detectar cambios de sincronización generalizada entre dos series temporales caóticas. En un segundo utilizamos redes echo-state para caracterizar la no estacionaridad de un fenómeno considerado de estado estable, potenciales visuales evocados steady-sate (SSVEP). Finalmente en un tercer estudio proponemos un nuevo biomarcardor para TDAH capaz de cuantificar cambios en la dinámica de la señal EEG entre condiciones bajas y normales de excitación. Los resultados aquí presentados demuestran la excelente capacidad de detección de patrones no estacionarios de estas redes, así como su aplicabilidad en el análisis de datos electrofisiológicos.

碩士
國立中央大學
電機工程研究所
95
The present study proposes a new visual evoked potential (VEP)-based brain computer interface (BCI). Users gaze at different spatially separated flash channels (FCs) in order to induce visual evoked signals that have temporal sequences corresponding to the gazed FCs, so that the gazed FC can be recognized and the command mapping to the gazed FC can be sent out to achieve control purposes. To achieve distinct flickering sequences among different FCs, we utilized different frequencies and phases to encode the flashing sequences of different FCs. The proposed system provides the high flexibility in expansion of FC number and high information transfer rate (ITR) which are superior to the traditional SSVEP-based and FVEP-based BCIs. In this thesis, we have built an eight-FC system. The command transfer rate and detected accuracy are 0.52 sec/command and 100%, respectively.

The effect of visual attention on neural signals has been extensively studied using various techniques such as macaque neurophysiology and human electro/magneto encephalogram (EEG/MEG). Depending on the technique, different neural measures are typically used for studying attention. For example, in neurophysiology experiments involving macaques, many studies have focused on the modulation in spiking activity or the change in oscillatory power at different frequency bands such as alpha (8-12 Hz) or gamma (30-80 Hz) with attention, or the change in the relationship of spikes with these oscillations. In contrast, human EEG studies, in addition to studying alpha and gamma modulation, often use flickering stimuli that produce a specific neural response called steady-state visually evoked potential (SSVEP), which is also modulated by attention. However, due to the differences in stimuli and task paradigms in such studies, it is difficult to determine the effectiveness of these various neural measures for capturing attentional modulation. To address this, we designed a task paradigm which included both static and counterphase flickering stimuli to generate all the relevant neural measures (alpha/gamma power as well as SSVEPs) under identical recording conditions, which allowed us to compare their effectiveness in studying attention. Since several reports suggest that attention modulates these neural measures through a canonical neural mechanism called normalization, in the first study of this thesis, we varied the normalization strength parametrically as a proxy for attentional modulation and tested its effect on various neural measures. We manipulated normalization strength by presenting static as well as flickering orthogonal superimposed gratings (plaids) at varying contrasts to two female monkeys while recording multiunit activity (MUA) and LFP from the primary visual cortex (area V1). We quantified the modulation in MUA, gamma (32-80 Hz), high-gamma (104-248 Hz) power, and SSVEP. Even under similar conditions, normalization strength was different for the four measures; and increased as: spikes, high-gamma, SSVEP, and gamma. However, these results could be explained using a normalization model, modified for population responses by varying the tuned normalization parameter and semi-saturation constant. In the second part of the thesis, we tested the predictions of the gamma phase coding hypothesis in the context of stimulus contrast and visual attention. The gamma phase coding hypothesis posits that the intensity of the incoming stimulus is encoded in the position of the spike relative to the gamma rhythm. Using chronically implanted microelectrode arrays in the primary visual cortex of macaques engaged in an attention task while presenting stimuli of varying contrasts, we tested whether the phase of the gamma rhythm relative to spikes varied as a function of stimulus contrast and attentional state. We analyzed spikes and LFP from different electrodes and found a weak but significant effect of attention, but not stimulus contrast, on the gamma phase relative to spikes. Although we found a significant effect of attention, we argue that a small magnitude of phase shift as well as the dependence of phase angles on gamma power and center frequency, limits the potential role of gamma in phase coding in area V1. In the third part of the thesis, we recorded EEG signals from 26 human participants while they were engaged in an attention task and analyzed alpha and gamma band powers for both static and flickering stimuli and SSVEP power for flickering stimuli. We report two main results. First, attentional modulation was comparable for SSVEP and alpha. Second, we found that non-foveal stimuli produced weak gamma despite various stimulus optimizations and therefore showed a negligible effect of attention although the same participants showed robust gamma activity for full-screen gratings. Thus, alpha and SSVEP won over gamma in capturing attentional modulation in human EEG. This result was in contrast to the findings of a comparable study in monkeys, where gamma and alpha won over SSVEPs. This study highlights the effectiveness of various neural measures in studying visual spatial attention and further implicates their usefulness in decoding behavior and attentional state in humans.
DBT-Wellcome Trust India Alliance (Grant IA/S/18/2/504003), Tata Trusts, DBT-IISc Partnership Programme

Dissertação de Mestrado Integrado em Engenharia Electrotécnica e de Computadores apresentada à Faculdade de Ciências e Tecnologia
A interface cérebro-computador (ICC) permite a comunicação direta entre o cérebro e um computador digital. Os sistemas de ICC ainda não são exequíveis para o uso diário e, portanto, estãoconfinados principalmente à experimentação em laboratório. É necessário converter os sistemasICC em dispositivos portáteis, confiáveis e independentes que possam ser usados em aplicativosdo mundo real fora do laboratório, para que possam atender a potenciais usuários-alvo, em aplicativos de comunicação / controlo para pessoas com deficiências motoras graves, ou como umaferramenta para a neuro-reabilitação de distúrbios motores ou distúrbios do desenvolvimento neurológico. Neste trabalho, propõe-se o desenvolvimento de um sistema ICC que possa controlardispositivos domóticos com base em um equipamento de open source (OpenBCI). Um dos principais objetivos desta tese é avaliar se este dispositivo fornece alguns dos recursos desejados paraum ICC, no que diz respeito ao desgaste, operação autónoma de confiabilidade e baixo custo. OICC proposto é baseado num neuromecanismo chamado potencial evocado visual em estado estacionário (PEVEE), que é estimulado por estimulação visual. Vários métodos de processamentode sinal (FFT, Welch, CCA-Standard, CCA-IT, CCA-Comb e CCA-Lite) foram implementadose comparados para extrair características do PEVEE. Inicialmente, os métodos de processamentode sinal foram testados offline em bancos de dados de referência e, em seguida, testados offline eonline com nossa estrutura / configuração do ICC. Foram comparados dois tipos de estimulação,um provocado por uma matriz de LEDs e o outro provocado por flashes de ecrã. Os resultadosoffline mostraram que a Análise Combinacional de Correlação Canónica atinge melhor precisãoem comparação com os outros métodos de extração de recursos, mas em termos de tempo de processamento do sinal, o método CCA-Lite diminui para 58 % do CCA-Comb. Os resultados obtidosonline mostraram ser possível controlar o ICC com uma precisão de 84,6 % (para segmentos deEEG de 5 segundos), o que mostra a viabilidade do sistema, embora várias limitações do sistemaOpenBCI tenham sido identificadas.
Brain-Computer Interface (BCI) allows the direct communication between the brain and a digital computer. BCIs systems are still impractical for everyday use and therefore are mostly confinedto lab experimentation. There is a need to convert BCI systems into wearable, reliable and standalone devices that can be used in real-world applications outside the lab, so that they can servepotential target users, in communication/control applications for people with severe motor disabilities, or as a tool for neurorehabilitation of motor disorders or neurodevelopmental disorders. Inthis work, it is proposed the development of a BCI system that can control domotic appliancesbased on an open-source equipment (OpenBCI). One of the main goals of this thesis is to evaluatewhether this device provides some of the desired features for a BCI, regarding wearability, reliability stand-alone operation and low-cost. The proposed BCI is based on a neural mechanism calledsteady-state visual evoked potential (SSVEP) which is elicited by visual stimulation. Several signal processing methods (FFT, Welch, CCA-Standard, CCA-IT, CCA-Comb and CCA-Lite) wereimplemented and compared to extract SSVEP features. Initially, the signal processing methodswere tested offline on benchmark datasets and then they were tested offline and online with ourBCI setup/framework. Two types of stimulation were compared, one elicited by a LED matrix andthe other elicited by screen flashes. Offline results showed that Combinational Canonical Correlation Analysis achieves the best accuracy in comparison to the other feature extraction methods,but in terms of signal processing time, the CCA-Lite method decreases to 58% of CCA-Comb.The results obtained in online experiments showed that it was possible to control the BCI with anaccuracy of 84.6% (for 5 second identification EEG segments), which shows the feasibility of thesystem, although several limitations of the OpenBCI system were identified.

M. Tech. Electrical Engineering
Determines whether Hidden Markov models (HMM) can be used to classify steady state visual evoked electroencephalogram signals in a BCI system. This is for the purpose of aiding disabled people in driving a wheelchair.

Electrical signals from the brain can be recorded at several different scales, ranging from spiking activity to local field potential (LFP) in animals to scalp electroencephalogram (EEG) in humans. Each signal represents a progressively larger level of neural integration and is thought to reflect different attributes of the underlying neural population. In this work, we characterized the relationships between these signals using a common paradigm of temporally modulated visual stimuli, which are known to engage the underlying neural activity in distinct ways. In the first part, we asked whether the LFP reflects the input or the output of a cortical area around the recording microelectrode. Using chronically implanted arrays in the primary visual cortex (V1) of awake behaving macaque monkeys, we recorded spikes and LFP in response to drifting sinusoidal gratings of varying temporal frequency. Previous reports have shown that the primate lateral geniculate nucleus (LGN) which projects to V1 has a higher temporal frequency cutoff than V1, such that at higher drift rates, visual input to V1 persists but V1 output ceases, permitting partial dissociation. Using an adaptive decomposition technique called Matching Pursuit (MP) to generate the time-frequency spectrum of the LFP at high resolution, we show that distinct frequency bands in the V1 LFP are tuned differently to temporal frequency, such that the lower frequencies of the LFP (up to ~50 Hz) likely represent the input, the gamma band of the LFP (~30-80 Hz) likely represents local cortical processing and the high-gamma band (above ~80 Hz) represents the output. In the second part, we asked whether steady-state visually evoked potential (SSVEP) tags used in “frequency tagging” EEG studies of visual attention are independent or not. In these studies, it is observed that paying attention to one stimulus increases the amplitude of the SSVEP at the tagging frequency of that stimulus and simultaneously decreases the SSVEP amplitude at the unattended frequency. This has been explained using a “push-pull” or “spotlight” mechanism of attention. However, it is unclear whether changes in SSVEP amplitude could arise due to the presence of competing temporal frequencies without any top-down cognitive modulation, and whether this depends on the separation between the tagging frequencies or the features of the stimuli such as their orientations. To address these questions, we recorded spikes and LFP from V1 as well as EEG from awake behaving macaque monkeys while they passively fixated plaid stimuli whose components counterphased at different temporal frequencies. We observed reliable SSVEP response suppression, but the suppression was much greater for lower competing temporal frequencies than for higher ones. Further, the strength of this asymmetry depended on the relative orientation difference between the plaid components, with similar orientations causing significant suppression and orthogonal orientations causing little or no suppression. In the third part, we show that the well-known normalization model, adapted to SSVEP responses, provides a good account of temporal frequency suppression as a function of the difference in temporal frequencies and orientation. Our results provide evidence for interaction between temporal frequencies independent of effects of cognitive modulation and suggest exercising caution in the interpretation of frequency tagging studies.