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Brain-Computer Interfaces: a technical approach to supporting privacy Kirsten Wahlstrom1,2, N. Ben Fairweather2, Helen Ashman1 1 School of Computer and Information Science University of South Australia South Australia 5095 Australia 2 Centre for Computing and Social Responsibility De Montfort University Leicester LE1 9BH UK Abstract Brain-Computer Interfaces (BCIs) are an emerging technology with implications for privacy. However, so far there have been no technical approaches to supporting the privacy of BCI users reported in the literature. An initial conceptual model for such a technical approach is presented for consideration in this paper. The initial conceptual model has three foundations. Firstly, BCI technologies are reviewed and technical components relevant to interoperability are identified. Secondly, privacy is conceptualised as a measurable requirement predicated upon enculturation, personal preference and context. Finally, the European Union’s privacy directives are reviewed to clarify legal context and requirements. As the suggested conceptual model is the first of its kind, analysis and critique are invaluable and are fostered through three discussion themes. The paper concludes with suggestions for further research. 1. Introduction Brain-Computer Interfaces (BCIs) provide a communication pathway between a brain and an external electronic device. Warwick’s self-experiments [Warwick and Gasson, 2004] demonstrated the technical feasibility of extending the peripheral nervous system, via the Internet, to other people and to external devices. Also, there have been recent advances in interpreting spontaneous neural activity [Coffey et al, 2010] and in concurrent interpretation of more than one intentional neural activity [Allison et al, 2010, Leeb et al, 2011]. Furthermore, there has been research into BCIs that stimulate perceptions in people with acquired blindness [Schmidt et al, 1996]. To the best of our knowledge, a BCI that stimulates human perceptions via the Internet remains undeveloped, although the popular press reports US Department of Defence research projects investigating ‘synthetic telepathy’ [Drummond, 2009, Shachtman, 2008] and there have been experiments in stimulating motor intent with animal models [London et al, 2008, Mavoori et al, 2005, Talwar et al, 2002]. A future BCI (fBCI) integrating these technical features would concurrently interpret both intentional and spontaneous neural activity, and it would stimulate perceptions. This would facilitate communication, via the Internet, between humans and also between humans and external devices. In addition to these research advances, BCIs interpreting intentional neural activity via electroencephalography (EEG) are available to consumers [Emotiv Systems, 2011, Intendix, 2011]. In a society, privacy is limited because participation necessitates communication, which results in some observation. When a person has the minimum possible privacy, they are under continuous observation and perhaps even scrutiny. Observation and scrutiny restrict autonomy and when autonomy is restricted, behaviours normalise and freedom and identity are compromised. Thus, the pursuit of freedom through autonomy requires that privacy be available. Given the research advances outlined above and the commercial availability of BCIs, an ethical and legal obligation to support the privacy of BCI users exists. Should an fBCI be commercially viable, the obligation will be pressing. This paper offers an initial conceptual model for refinement towards a technical approach to meeting this obligation. In related work, autonomy and identity are the focus of an argument for monitoring the development of BCI technologies from an ethical perspective [Lucivero and Tamburrini, 2008]. Additionally, an experiment using a simulated BCI to examine participants’ responses to stimuli concluded that “... one can be fooled into believing that one had an intention that one did not in fact have” [Lynn et al, 2010], a finding that suggests significant implications for autonomy. Furthermore, the human brain’s plasticity renders users of BCIs vulnerable to long-term restrictions of autonomy [Salvini et al, 2008]. Privacy has been specifically identified as an ethical issue relevant to BCIs [Wolpaw et al, 2006]. However, the only detailed discussion of privacy and BCIs [Denning et al, 2009] conflates privacy with data security and therefore conceptualises privacy as being susceptible to malicious attacks, whereas it is also at risk of unintentional, and even well-meaning, transgressions. In this paper, we propose an initial conceptual model that might be developed into one for a technical approach to supporting privacy in BCIs. In order to facilitate interoperability, the initial conceptual model is premised upon an understanding of the technical components of BCIs. It is also premised upon a conceptualisation of privacy as a perception which differs from person to person and which changes according to circumstances. Finally, in order to foster uptake, the initial conceptual model aims to enable compliance with the European Union’s various directives on privacy (see section 3). The paper puts forward the initial conceptual model, which is the first of its kind and may therefore be insufficient. Thus, a secondary contribution is an opportunity for scrutinising the initial conceptual model via stimulation of discussion and critique. These contributions may inform the design of a prototype for future implementation. If so, the prototype may be implemented and tested to measure the extent to which it is usable and the extent to which it enables compliance with the European Union’s privacy directives. The rest of this paper is organised as follows. Section two describes relevant technical components of BCIs and section three establishes a conceptualisation of privacy and reviews the European Union’s privacy directives. Together, these two sections provide a technical, conceptual and regulatory background for section four, which describes the initial conceptual model. Section five poses questions to stimulate discussion and critique. Section six concludes the paper by identifying options for future research projects. 2. BCI technology The cerebral cortex provides sensory and motor functioning, reasoning, planning and language [Nijholt et al, 2008]. BCIs identify and measure the electrical activity associated with activating specific neural pathways in the cerebral cortex [Berger et al, 2007]. Measurements of activity are then applied to the control of external devices, bypassing the peripheral nervous system [Hochberg et al, 2006]. Although there have been advances in interpreting spontaneous neural activity [Coffey et al, 2010], a brain generates a profusion of concurrent neural activity and separating a specific intention from neural ‘noise’ is difficult [Curran and Stokes, 2003]. Therefore, most BCIs require users to target neural activity at specific outcomes rather than sending and receiving information via the peripheral nervous system. Learning to direct thoughts in a way that can be understood by a BCI can take months and machine learning has been applied to relieve the burden of this task [Müller et al, 2007]. 2.1 Brain imaging Thought occurs when neurons in the brain send electrical signals. When sending or receiving an electrical signal, neurons require an increased supply of oxygen and glucose [New Scientist, 2011]. Therefore, an increase in blood flow occurs. Brain imaging technologies detect and depict increases in blood flow or electrical activity in order to illustrate brain functions. However, brain imaging does not enable observation of meaning; it depicts the type of neural activity only (examples include motor intent and visual perception) [Nijholt et al, 2008]. Thus, when brain imaging is applied in a BCI, semantic interpretation is provided in the BCI’s engineering. 2.2 Machine learning BCIs incorporating a machine learning component to map a user’s neural signals to their intentions have to be trained to recognise and classify a specific neural signal [Krusienski et al, 2011]. For example, consider a scenario in which Alice has purchased a new BCI to use with her mobile phone (This is not an unlikely scenario. BCIs have been applied to the control of mobile phones [Campbell et al, 2010]). Alice must train the BCI to: identify each unique pattern of neural activity that corresponds to each person in her mobile phone’s address book; classify a specific neural event as representative of a specific person; identify unique patterns of neural activity that correspond to the ‘call’ and ‘hang up’ intentions; and, lastly, to map the ‘call’ and ‘hang up’ intentional neural activities to the correlating functions provided by the mobile phone. In BCIs, machine learning uses pattern recognition to identify and classify real time neural activity. In pattern recognition, a ground truth function classifies previously unknown data [Müller et al, 2007]. In order to approach optimal performance, real-time data must display recognisable features. With respect to BCIs, the real-time data represents a human’s neural activity and therefore recognisable features in the real-time data may be obscured by noise. While there is some scope for instructing a human participant in using a BCI, by virtue of the brain’s capacity for multi-tasking, the deliberate production of a neural signal displaying recognisable features is intellectually taxing. Outcomes vary from person to person, with some people never achieving sufficient signal intensity [Curran and Stokes, 2003]. 3. Privacy When using technologies, people try to create and maintain privacy to assert freedom, identity and autonomy. The creation and maintenance of privacy can be achieved by declining to participate, or by using anonymity, pseudonymity or misinformation [Fuster, 2010, Lenhart and Madden, 2007]. When people opt out, adopt anonymity or pseudonymity, or engage in misinformation, the effectiveness of any technology reliant upon accurate and representative data is compromised. 3.1 Individual people Privacy expectations are shaped in three ways. Firstly, privacy emerges from a society’s communication practices [Westin, 2003]. For example, in some cultures, a house offers an opportunity to withdraw from the community, whereas in others, a community shares housing in an ad hoc manner. Thus, the extent to which a person expects privacy in a specific context emerges from their enculturation. Secondly, in addition to enculturation, privacy expectations are informed by personal preferences [Gavison, 1980]. In certain contexts, a person living in a culture of shared housing may require more privacy than others. Therefore, while cultural norms are influential, privacy expectations are diverse. Finally, a person’s expectation of privacy is dependent on changes in their immediate context [Solove, 2006]. For example, Bob expects complete privacy, even uninterrupted solitude, in his morning shower and conversely, very little in a busy shopping mall. His expectation of privacy differs according to context and a change to that context may cause a change in his privacy expectation. If he is alone during his morning shower, there is no difference between his privacy expectation and his perception of privacy; however, if someone were to enter the bathroom unexpectedly, the difference between Bob’s privacy expectation and his privacy perception would grow and a requirement for more privacy would be catalysed. Thus, a person’s privacy requirement, pr , can be defined in part as the difference between their privacy expectation, pe , and their immediate perception of privacy, p p pr pe p p (Equation 1) Thus, when pr 0 privacy equilibrium appears to exist. When pr 0 , privacy expectation is less than privacy perception and there is more privacy available than the person believes they need. Finally, when pr 0 , privacy expectation is greater than privacy perception and there is a requirement for more privacy. 3.2 Regulating privacy As privacy is a necessary enabler of important freedoms, it is logically required for it to be available to citizens of those nations upholding human rights and pursuing emancipation. The conceptualisation of privacy as emerging from enculturation and as unique for each person and their immediate context is well understood, long-standing, and widely applied by law and policy makers. It forms the basis for legislative and other regulatory approaches such as the Australian Privacy Act, the European Union’s privacy directives and the OECD’s guidelines. These legal obligations and further ethical obligations [Floridi, 2006] mandate support for privacy with respect to technologies. In order to foster uptake of a future privacy-enhancing technology, possibly based upon the initial conceptual model, this project aims inter alia to enable compliance with the European Union’s Privacy Directives and therefore an overview of them is necessary. The European Union (EU) has published four directives relevant to data privacy, which are colloquially known as the data protection directive [European Parliament and the Council of the European Union, 1995], the e-privacy directive [European Parliament and the Council of the European Union, 2002], the data retention directive [European Parliament and the Council of the European Union, 2006] and the cookie (or citizen’s rights) directive [European Parliament and the Council of the European Union, 2009]. Briefly, the data protection directive “... requires Member States to protect the rights and freedoms of natural persons with regard to the processing of personal data, and in particular their right to privacy, in order to ensure the free flow of personal data in the Community” [European Parliament and the Council of the European Union, 1995]. The e-privacy directive “... translates the principles set out in [the data protection directive] into specific rules for the electronic communications sector” [European Parliament and the Council of the European Union, 2002]. The data retention directive amends the e-privacy directive, addressing “... the retention of data generated or processed in connection with the provision of publicly available electronic communications services or of public communications networks” [European Parliament and the Council of the European Union, 2006]. Finally, the cookie directive also amends the e-privacy directive, requiring consent for cookies installed on users’ devices [European Parliament and the Council of the European Union, 2009]. To summarise, the cookie and data retention directives amend the e-privacy directive, which is an interpretation of the data protection directive. Thus, the data protection directive is the legal foundation for supporting data privacy in the EU. The data protection directive aims to enable unimpeded flow of data between EU member states. It applies to the processing of personal data, that is, data describing natural people. The directive makes no distinction between whether data is processed manually or automatically, except that it requires manual data processing (that is, human intervention) when legally binding decisions are being made. The directive ensures that processing of personal data meets three conditions: transparency, legitimacy of purpose and proportionality [European Parliament and the Council of the European Union, 1995]. Transparency means that a person is explicitly informed of the specific purpose when their personal data is processed. Legitimacy of purpose means that the purposes for which personal data are processed are legitimately related to the business needs of the data controller. Proportionality means that personal data must be processed only to an extent compatible with the explicitly stated purpose. Finally, the data protection directive restricts the transfer of data to settings that provide comparable levels of privacy protection. Thus, its breadth of influence extends beyond the EU’s member nations, requiring those wishing to process data about EU citizens to set up regimes to protect that data. The EU’s approach to data privacy is consequently the most comprehensive attempt to support privacy to date. 4. Initial conceptual model The initial model has two main conceptual goals: to be interoperable with BCI technologies, and to support privacy (which is unique for each person and changes according to circumstances). As privacy requirements differ from person to person and over time, a conceptual model that neglects flexibility cannot efficiently serve a wide user base. Therefore, BCIs incorporating a machine learning component to map a user’s neural signals to their privacy requirements are of relevance to this project. If a BCI’s pattern recognition component can detect a person’s neural activity, then it logically might be able to detect their privacy requirement. The privacy requirement can then be applied to any information being shared. For example, consider a scenario in which Bob is using a BCI to interact with his mobile phone. He is calling Charlie but does not want the call to be logged in the mobile phone’s storage. First, he thinks of Charlie and the mobile phone retrieves Charlie’s number. Then Bob thinks of not logging the call and the mobile phone saves this privacy requirement in its working memory. Finally, Bob thinks ‘call’ and the mobile phone places the call without logging it and clears its working memory. This scenario has only two outcomes: log the call or don’t log the call. It also relies on a conscious decision to indicate privacy is required. However, privacy requirements are more diverse than this. The definition for privacy requirements (see equation (1) above) enables a diversity of privacy requirements and can be used to inform the initial conceptual model. Equation (1) accounts for a person seeking no change in, less, or more privacy. For example, Alice has a BCI which she plans to use for enabling data privacy. In a training phase conducted in controlled circumstances, the BCI’s pattern recognition component establishes a ground truth function to identity her neural activity corresponding to the three privacy aspiration states defined by equation (1). Then, in an operational setting, the BCI’s pattern recognition component classifies Alice’s real time neural activity via the ground truth function. Alice’s real time context-specific desire for no change in, less or more privacy can then be applied to communications with external devices. However, in practice, it may be that a person rarely actively seeks less privacy. Therefore, in an implementation rather than specifying a requirement for less privacy, an approval of a privacy reduction may be more appropriate. Figure 1 provides an overview of this conceptual approach; Figure 2 illustrates the initial conceptual model’s training phase in which a person deliberately thinks about no change in privacy, approving a privacy reduction and more privacy in turn; Figure 3 illustrates the initial conceptual model’s operational use in which a person spontaneously thinks about no change in privacy, approving a privacy reduction or more privacy, according to circumstances. To continue the previous example, if Alice requires more privacy, it can be provided; and if she continues to require more privacy, more can be provided; eventually she will require no change in privacy. Thus, the importance of accurately detecting the ‘no change’ neural state is clear: it is the stopping condition and it enables the extent to which Alice requires privacy in that given context to be measured by the BCI. However, the human brain has a high degree of plasticity and a person’s neural patterns corresponding to ‘no change in’, ‘less’ or ‘more’ privacy provision may not remain constant over time. In order to adapt to small but constant changes in neural activities, the BCI’s pattern recognition component can be configured to intermittently recalibrate its ground truth function. This requires that Alice receive feedback from the BCI and that she confirm or deny its interpretation of her privacy aspirations. A BCI providing haptic feedback was found to better enable attentiveness and accuracy when compared to a BCI providing visual feedback [Cincotti et al, 2008]. A similar approach may be useful in providing feedback for Alice’s recalibration of the BCI’s ground truth function. The initial conceptual model appears to achieve its main goals. The model satisfies the condition that it be interoperable with BCI technologies because it leverages a BCI’s preexisting pattern recognition component. As the initial conceptual model requires that the ground truth function be generated for each person using the BCI, it enables privacy requirements to differ from person to person. Furthermore, as the model requires that the ground truth function be intermittently recalibrated, it also supports privacy requirements that change over time. In addition, the initially conceptual model meets the transparency, legitimacy of purpose and proportionality conditions of the EU’s data protection directive as it responds to the BCI user’s immediate privacy requirements, rather than the data collection objectives of the data controller. 5. Discussion and critique The initial conceptual model presented above is the first of its kind and may provide a foundation for future research, informing the design and implementation of a privacyenhancing technology (PET) for BCIs. Thus, analysis and critique of the model are essential, welcome, and are fostered here through preliminary identification of some of the model’s questions for theory, legal and regulatory frameworks, and its operational and technical problems. We welcome suggestions of other questions for theory, legal and regulatory frameworks, and its operational and technical problems. Ground truth function Training Operational use Figure 1: Overview of initial conceptual model. Controlled environment More privacy No change in privacy Machine learning Ground truth function Less privacy is OK Figure 2: Training phase. Uncontrolled environment Ground truth function More privacy No change in privacy Less privacy is OK Figure 3: Operational use. Privacy requirement 5.1 Theory The initial conceptual model is interesting from the perspectives of neuroethics and transhumanism. Neuroethics encompasses two themes: arguments that focus on the ethical issues emerging from neuroscience and its products, and arguments emerging from the ways in which advances in neuroscience enable reconsideration of long-standing philosophical problems [Levy, 2008]. Transhumanism is a related area of investigation. Transhumanists theorise the impacts of leveraging technologies in human evolution, envisaging the effects of enhancing human intelligence and physical and psychological capacities [Agar, 2007]. BCIs have emerged from a range of research disciplines, one of which is neuroscience. While enabling at least some elements of autonomy in terms of privacy, the initial conceptual model might facilitate the uptake of BCI technologies. Thus, it poses a wide range of neuroethical problems. For example, BCIs restrict autonomy to the extent that BCI users may not always be able to determine whether an intention originates with themselves [Lynn et al, 2010] and loss of autonomy is linked to the loss of freedom; therefore, do the initial conceptual model’s benefits outweigh any loss? Is such a utilitarian analysis of the initial conceptual model appropriate? Should a prototype of the conceptual model be implemented after it has been further developed? If so, to what extent should it test for loss of autonomy and identity? With respect to transhumanism, BCIs extend the capabilities of the human nervous system beyond its biological limits. Should the eventual conceptual model enable BCIs to be used in humanly ways? If so, to what extent should it support humanly ways of using BCIs? There may be other ethical questions arising from these avenues of enquiry. We welcome suggestions of what they might be. 5.2 Legal and regulatory frameworks One of the initial conceptual model’s goals was to foster compliance with the EU’s privacy directives. However, the directives were not examined in detail, instead the three conditions of the data protection directive sufficed to inform legal context. Does the initial conceptual model overlook relevant features in the EU’s privacy directives? If so, how may it be amended? 5.3 Operational and technical problems The initial conceptual model is susceptible to noisy data, user fatigue and an unwieldy semantic burden. It might be that these are such substantial problems as to prevent practical implementation, and it may be that other technical problems remain to be identified. As noted above, machine learning would be applied to transfer the learning curve from the user to the BCI. However, in the initial conceptual model, the ground truth function is derived under controlled conditions. As operational conditions involve interference and noisy data, the ground truth function may produce unreliable inferences. Such inferences may lead to inaccuracies in the extent to which privacy is provided. A person using an implementation developed from a refinement of the conceptual model may have to concentrate in order to produce a sufficiently clear signal, which may cause fatigue. Once fatigued, the person’s capability of producing subsequent signals will be reduced. Assuming these problems are overcome, and a person’s privacy requirement can be accurately identified, there remains the issue of how best to support their privacy. The initial conceptual model has a semantic capability limited to three indicators of privacy requirements, yet it may be used with a BCI performing any communicative task. If the BCI has been designed with a rich semantics of its domain of use, applying a suitable privacy enhancing technology (for example, encryption) is a trivial task. Otherwise, the burden of deploying an appropriate privacy enhancing technology may have to rest with the user. 6. Conclusion This paper contributes an initial conceptual model for a technical approach to supporting the privacy of BCI users. The model offers interoperability with existing BCI technology. Also, it is premised upon a view of privacy as unique for each person and changing over time. Lastly, the model could foster compliance with the transparency, legitimacy of purpose and proportionality conditions of the EU’s data protection directive. A preliminary analysis and critique of the model has been attempted, but many questions remain and feedback is welcome. Future research will apply the critique stimulated by this paper to the initial conceptual model. Later, the developed and refined (or re-designed) conceptual model may be used to inform the design of a PET for BCIs. If a design is feasible and sufficient, a prototype may be implemented and tested for usability. 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